idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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42,301 | Predictor and error are independent | Subscripts and clear exposition of dependencies matter. We investigate the prediction error. We construct our predictor based on data $\{(x^{(1)},y^{(1)}),\dots,(x^{(m)},y^{(m)})\}$, so write $\hat f = \hat f_m$ to remember that. We then consider predicting
$$y^{(m+1)} = f[x^{(m+1)}]+ \epsilon^{(m+1)}$$
based on $x^{(... | Predictor and error are independent | Subscripts and clear exposition of dependencies matter. We investigate the prediction error. We construct our predictor based on data $\{(x^{(1)},y^{(1)}),\dots,(x^{(m)},y^{(m)})\}$, so write $\hat f | Predictor and error are independent
Subscripts and clear exposition of dependencies matter. We investigate the prediction error. We construct our predictor based on data $\{(x^{(1)},y^{(1)}),\dots,(x^{(m)},y^{(m)})\}$, so write $\hat f = \hat f_m$ to remember that. We then consider predicting
$$y^{(m+1)} = f[x^{(m+1)}... | Predictor and error are independent
Subscripts and clear exposition of dependencies matter. We investigate the prediction error. We construct our predictor based on data $\{(x^{(1)},y^{(1)}),\dots,(x^{(m)},y^{(m)})\}$, so write $\hat f |
42,302 | Why are machine learning algorithms performing worse than standard multiple linear regression? | Assumptions give you power - when they are valid.
When the assumptions of a linear regression (or any other simple model) are fulfilled, they will outperform more complex and flexible models. Let's consider a model with a single predictor. If the relationship is linear, a linear regression will quite likely outperform ... | Why are machine learning algorithms performing worse than standard multiple linear regression? | Assumptions give you power - when they are valid.
When the assumptions of a linear regression (or any other simple model) are fulfilled, they will outperform more complex and flexible models. Let's co | Why are machine learning algorithms performing worse than standard multiple linear regression?
Assumptions give you power - when they are valid.
When the assumptions of a linear regression (or any other simple model) are fulfilled, they will outperform more complex and flexible models. Let's consider a model with a sin... | Why are machine learning algorithms performing worse than standard multiple linear regression?
Assumptions give you power - when they are valid.
When the assumptions of a linear regression (or any other simple model) are fulfilled, they will outperform more complex and flexible models. Let's co |
42,303 | Can the differential entropy be negative infinity? | Yes, there are densities with negatively infinite entropy.
To construct one, find a family of densities with arbitrarily negative entropy. (Clearly this requires the density functions to have arbitrarily large values and therefore there are points where they become "singular.") By shifting and scaling them into disjoin... | Can the differential entropy be negative infinity? | Yes, there are densities with negatively infinite entropy.
To construct one, find a family of densities with arbitrarily negative entropy. (Clearly this requires the density functions to have arbitrar | Can the differential entropy be negative infinity?
Yes, there are densities with negatively infinite entropy.
To construct one, find a family of densities with arbitrarily negative entropy. (Clearly this requires the density functions to have arbitrarily large values and therefore there are points where they become "si... | Can the differential entropy be negative infinity?
Yes, there are densities with negatively infinite entropy.
To construct one, find a family of densities with arbitrarily negative entropy. (Clearly this requires the density functions to have arbitrar |
42,304 | Can the differential entropy be negative infinity? | I actually came across to this problem from a different angle. I was trying to understand why the mutual information of multivariate normal distribution goes to $\infty$ as the correlation goes to 1. See here
So to your question, sorry I don't know the answer, but if we loose the condition $H(f)=-\infty$ to any arbitra... | Can the differential entropy be negative infinity? | I actually came across to this problem from a different angle. I was trying to understand why the mutual information of multivariate normal distribution goes to $\infty$ as the correlation goes to 1. | Can the differential entropy be negative infinity?
I actually came across to this problem from a different angle. I was trying to understand why the mutual information of multivariate normal distribution goes to $\infty$ as the correlation goes to 1. See here
So to your question, sorry I don't know the answer, but if w... | Can the differential entropy be negative infinity?
I actually came across to this problem from a different angle. I was trying to understand why the mutual information of multivariate normal distribution goes to $\infty$ as the correlation goes to 1. |
42,305 | Why do Variational Bayes methods assume that the likelihood $p(x|z)$ is tractable while the posterior is not? | The likelihood is often tractable because we are free to pick it and we pick something that is easy to work with. If it was not tractable, we could not compute it and could not use the model.
Note that the posterior and likelihood are coupled through Bayes theorem:
$$p(z|x) = \frac{p(x|z) p(z)}{p(x)}.$$
The consequenc... | Why do Variational Bayes methods assume that the likelihood $p(x|z)$ is tractable while the posterio | The likelihood is often tractable because we are free to pick it and we pick something that is easy to work with. If it was not tractable, we could not compute it and could not use the model.
Note th | Why do Variational Bayes methods assume that the likelihood $p(x|z)$ is tractable while the posterior is not?
The likelihood is often tractable because we are free to pick it and we pick something that is easy to work with. If it was not tractable, we could not compute it and could not use the model.
Note that the pos... | Why do Variational Bayes methods assume that the likelihood $p(x|z)$ is tractable while the posterio
The likelihood is often tractable because we are free to pick it and we pick something that is easy to work with. If it was not tractable, we could not compute it and could not use the model.
Note th |
42,306 | Knowledge graph: how to get into it? | This set of notes provides a good overview of (some) of the work in the field of knowledge graphs and automated reasoning, including:
A history of ontology experiments on the web, i.e. attempts to extract structured relationships between online resources
A basic overview/tutorial of what knowledge graphs are and how ... | Knowledge graph: how to get into it? | This set of notes provides a good overview of (some) of the work in the field of knowledge graphs and automated reasoning, including:
A history of ontology experiments on the web, i.e. attempts to e | Knowledge graph: how to get into it?
This set of notes provides a good overview of (some) of the work in the field of knowledge graphs and automated reasoning, including:
A history of ontology experiments on the web, i.e. attempts to extract structured relationships between online resources
A basic overview/tutorial ... | Knowledge graph: how to get into it?
This set of notes provides a good overview of (some) of the work in the field of knowledge graphs and automated reasoning, including:
A history of ontology experiments on the web, i.e. attempts to e |
42,307 | Showing $S^2$ and $\overline{Y}$ are independent: seeking a solution to this textbook problem | I'm not sure what the authors have in mind, but the closest solution I can think of using (c) is to apply Cochran's theorem. Have you covered that, or maybe a special case of it?
Here's the proof using that:
Let $Z_i = \frac{Y_i - \mu}{\sigma}$ so $Z_i \sim \mathcal N(0, 1)$ and $\bar Z \sim \mathcal N(\mu, \sigma^2/n)... | Showing $S^2$ and $\overline{Y}$ are independent: seeking a solution to this textbook problem | I'm not sure what the authors have in mind, but the closest solution I can think of using (c) is to apply Cochran's theorem. Have you covered that, or maybe a special case of it?
Here's the proof usin | Showing $S^2$ and $\overline{Y}$ are independent: seeking a solution to this textbook problem
I'm not sure what the authors have in mind, but the closest solution I can think of using (c) is to apply Cochran's theorem. Have you covered that, or maybe a special case of it?
Here's the proof using that:
Let $Z_i = \frac{Y... | Showing $S^2$ and $\overline{Y}$ are independent: seeking a solution to this textbook problem
I'm not sure what the authors have in mind, but the closest solution I can think of using (c) is to apply Cochran's theorem. Have you covered that, or maybe a special case of it?
Here's the proof usin |
42,308 | Why do we use Vector Autoregressive Models? | Your question can be addressed under the Seemingly Unrelated Regression (SUR) theory. Normally if $\epsilon_t$ and $\eta_s$ are not correlated other than when $t=s$ the SUR theory suggests that there is no difference between estimating the equations separately or alltogether as long as all equations contain the same ex... | Why do we use Vector Autoregressive Models? | Your question can be addressed under the Seemingly Unrelated Regression (SUR) theory. Normally if $\epsilon_t$ and $\eta_s$ are not correlated other than when $t=s$ the SUR theory suggests that there | Why do we use Vector Autoregressive Models?
Your question can be addressed under the Seemingly Unrelated Regression (SUR) theory. Normally if $\epsilon_t$ and $\eta_s$ are not correlated other than when $t=s$ the SUR theory suggests that there is no difference between estimating the equations separately or alltogether ... | Why do we use Vector Autoregressive Models?
Your question can be addressed under the Seemingly Unrelated Regression (SUR) theory. Normally if $\epsilon_t$ and $\eta_s$ are not correlated other than when $t=s$ the SUR theory suggests that there |
42,309 | Why do we use Vector Autoregressive Models? | Trying a short non-technical answer. Univariate time series models like AR (or ARMA) for stationary time series try to autoproject the future of the series from its past. Thas is, your projections only comes from the history of the series. That may be fine, maybe you do not have any other information on other variables... | Why do we use Vector Autoregressive Models? | Trying a short non-technical answer. Univariate time series models like AR (or ARMA) for stationary time series try to autoproject the future of the series from its past. Thas is, your projections onl | Why do we use Vector Autoregressive Models?
Trying a short non-technical answer. Univariate time series models like AR (or ARMA) for stationary time series try to autoproject the future of the series from its past. Thas is, your projections only comes from the history of the series. That may be fine, maybe you do not h... | Why do we use Vector Autoregressive Models?
Trying a short non-technical answer. Univariate time series models like AR (or ARMA) for stationary time series try to autoproject the future of the series from its past. Thas is, your projections onl |
42,310 | Standard error of regression and of predictions in python (these are available in R) [closed] | To the last part
Several models have now a get_prediction method that provide standard errors and confidence interval for predicted mean and prediction intervals for new observations.
pred = results.get_prediction(x_predict)
pred_df = pred.summary_frame()
some examples are in this gist https://gist.github.com/josef-pk... | Standard error of regression and of predictions in python (these are available in R) [closed] | To the last part
Several models have now a get_prediction method that provide standard errors and confidence interval for predicted mean and prediction intervals for new observations.
pred = results.g | Standard error of regression and of predictions in python (these are available in R) [closed]
To the last part
Several models have now a get_prediction method that provide standard errors and confidence interval for predicted mean and prediction intervals for new observations.
pred = results.get_prediction(x_predict)
p... | Standard error of regression and of predictions in python (these are available in R) [closed]
To the last part
Several models have now a get_prediction method that provide standard errors and confidence interval for predicted mean and prediction intervals for new observations.
pred = results.g |
42,311 | Standard error of regression and of predictions in python (these are available in R) [closed] | For your first question, I think what R calls the "residual standard error" is the square root of the scale parameter:
np.sqrt(est_1a.scale) | Standard error of regression and of predictions in python (these are available in R) [closed] | For your first question, I think what R calls the "residual standard error" is the square root of the scale parameter:
np.sqrt(est_1a.scale) | Standard error of regression and of predictions in python (these are available in R) [closed]
For your first question, I think what R calls the "residual standard error" is the square root of the scale parameter:
np.sqrt(est_1a.scale) | Standard error of regression and of predictions in python (these are available in R) [closed]
For your first question, I think what R calls the "residual standard error" is the square root of the scale parameter:
np.sqrt(est_1a.scale) |
42,312 | Training Autoencoder with Softmax Layer | I don't know why this was downvoted, but I figured out the answer though it may be obvious.
The training set is used to train one compression/encoder layer by learning to approximate itself using the training set.
Once this is done, the weights / layer that is responsible for the encoding part is saved and paired with ... | Training Autoencoder with Softmax Layer | I don't know why this was downvoted, but I figured out the answer though it may be obvious.
The training set is used to train one compression/encoder layer by learning to approximate itself using the | Training Autoencoder with Softmax Layer
I don't know why this was downvoted, but I figured out the answer though it may be obvious.
The training set is used to train one compression/encoder layer by learning to approximate itself using the training set.
Once this is done, the weights / layer that is responsible for the... | Training Autoencoder with Softmax Layer
I don't know why this was downvoted, but I figured out the answer though it may be obvious.
The training set is used to train one compression/encoder layer by learning to approximate itself using the |
42,313 | Generating random sample from inverse Cdf without closed form | Here are two ways to compute numerical approximations to the inverse of the cdf, assuming that you have made choices for m,d,α,β and p. Both methods require that you can compute F(x) for a given x, so ...
m = 1
d = 2
a = 1
b = 2
p = 0.5
F = function(x) (1 - ((1+x^m)^-d) * exp(-b*x^a)) /
(1 - (p*(1+x^m)^-d) * exp... | Generating random sample from inverse Cdf without closed form | Here are two ways to compute numerical approximations to the inverse of the cdf, assuming that you have made choices for m,d,α,β and p. Both methods require that you can compute F(x) for a given x, s | Generating random sample from inverse Cdf without closed form
Here are two ways to compute numerical approximations to the inverse of the cdf, assuming that you have made choices for m,d,α,β and p. Both methods require that you can compute F(x) for a given x, so ...
m = 1
d = 2
a = 1
b = 2
p = 0.5
F = function(x) (1 -... | Generating random sample from inverse Cdf without closed form
Here are two ways to compute numerical approximations to the inverse of the cdf, assuming that you have made choices for m,d,α,β and p. Both methods require that you can compute F(x) for a given x, s |
42,314 | Likelihood ratio minimal sufficient | Consider $f(x|\theta)$ for $\theta \in \{\theta_1, \cdots, \theta_p\}$. It can be written as \begin{align*}
f(x|\theta)&=\frac{f(x|\theta)}{f(x|\theta_p)}\times f(x|\theta_p)\\
&=f(x|\theta_p)\,\exp\{\log[{f(x|\theta)}\big/{f(x|\theta_p)}]\}
\end{align*}
Since $\theta \in \{\theta_1, \cdots, \theta_p\}$, $\theta$ is on... | Likelihood ratio minimal sufficient | Consider $f(x|\theta)$ for $\theta \in \{\theta_1, \cdots, \theta_p\}$. It can be written as \begin{align*}
f(x|\theta)&=\frac{f(x|\theta)}{f(x|\theta_p)}\times f(x|\theta_p)\\
&=f(x|\theta_p)\,\exp\{ | Likelihood ratio minimal sufficient
Consider $f(x|\theta)$ for $\theta \in \{\theta_1, \cdots, \theta_p\}$. It can be written as \begin{align*}
f(x|\theta)&=\frac{f(x|\theta)}{f(x|\theta_p)}\times f(x|\theta_p)\\
&=f(x|\theta_p)\,\exp\{\log[{f(x|\theta)}\big/{f(x|\theta_p)}]\}
\end{align*}
Since $\theta \in \{\theta_1,... | Likelihood ratio minimal sufficient
Consider $f(x|\theta)$ for $\theta \in \{\theta_1, \cdots, \theta_p\}$. It can be written as \begin{align*}
f(x|\theta)&=\frac{f(x|\theta)}{f(x|\theta_p)}\times f(x|\theta_p)\\
&=f(x|\theta_p)\,\exp\{ |
42,315 | Expected survival time for Weibull proportional hazards model with R's predict.survreg | Your question is somewhat related to this question and particularly this question and the following answer by Therneau, Terry M.
The survreg routine assumes that log(y) ~ covariates + error. For a log-normal distribion the error is Gaussian and thus the
predict(fit, type='response') will be exp(predicted mean of... | Expected survival time for Weibull proportional hazards model with R's predict.survreg | Your question is somewhat related to this question and particularly this question and the following answer by Therneau, Terry M.
The survreg routine assumes that log(y) ~ covariates + error. For a | Expected survival time for Weibull proportional hazards model with R's predict.survreg
Your question is somewhat related to this question and particularly this question and the following answer by Therneau, Terry M.
The survreg routine assumes that log(y) ~ covariates + error. For a log-normal distribion the error ... | Expected survival time for Weibull proportional hazards model with R's predict.survreg
Your question is somewhat related to this question and particularly this question and the following answer by Therneau, Terry M.
The survreg routine assumes that log(y) ~ covariates + error. For a |
42,316 | How avoid regularizing intercept in scikit's LogisticRegression | Use a solver other than liblinear. The liblinear solver (formerly the default for LogisticRegression) regularizes the intercept, but the others do not (see https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression). If you must use liblinear, see the intercept_scaling argument, which presumably is i... | How avoid regularizing intercept in scikit's LogisticRegression | Use a solver other than liblinear. The liblinear solver (formerly the default for LogisticRegression) regularizes the intercept, but the others do not (see https://scikit-learn.org/stable/modules/line | How avoid regularizing intercept in scikit's LogisticRegression
Use a solver other than liblinear. The liblinear solver (formerly the default for LogisticRegression) regularizes the intercept, but the others do not (see https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression). If you must use libl... | How avoid regularizing intercept in scikit's LogisticRegression
Use a solver other than liblinear. The liblinear solver (formerly the default for LogisticRegression) regularizes the intercept, but the others do not (see https://scikit-learn.org/stable/modules/line |
42,317 | How avoid regularizing intercept in scikit's LogisticRegression | There is one quick and dirty fix to make less regularization on intercept.
To do it, we can append a huge number in intercept column say $1 \times 10^{9}$, instead of expand the data frame with all $1$ column.
For example, with mtcars data, if we want to do logistic regression respect to weight, we do
> head(cbind(1e... | How avoid regularizing intercept in scikit's LogisticRegression | There is one quick and dirty fix to make less regularization on intercept.
To do it, we can append a huge number in intercept column say $1 \times 10^{9}$, instead of expand the data frame with all $ | How avoid regularizing intercept in scikit's LogisticRegression
There is one quick and dirty fix to make less regularization on intercept.
To do it, we can append a huge number in intercept column say $1 \times 10^{9}$, instead of expand the data frame with all $1$ column.
For example, with mtcars data, if we want to ... | How avoid regularizing intercept in scikit's LogisticRegression
There is one quick and dirty fix to make less regularization on intercept.
To do it, we can append a huge number in intercept column say $1 \times 10^{9}$, instead of expand the data frame with all $ |
42,318 | Big difference between a t-test and a F-test in a mixed model (anova vs summary in lmerTest) | Type III tests require correct coding for lower-order effects to be meaningful, specifically orthogonal contrasts. The R default contr.treatment is not orthogonal, other contrasts are though (e.g., contr.sum). In your code it looks like you used did not change the defaults, hence your results are so-called simple main ... | Big difference between a t-test and a F-test in a mixed model (anova vs summary in lmerTest) | Type III tests require correct coding for lower-order effects to be meaningful, specifically orthogonal contrasts. The R default contr.treatment is not orthogonal, other contrasts are though (e.g., co | Big difference between a t-test and a F-test in a mixed model (anova vs summary in lmerTest)
Type III tests require correct coding for lower-order effects to be meaningful, specifically orthogonal contrasts. The R default contr.treatment is not orthogonal, other contrasts are though (e.g., contr.sum). In your code it l... | Big difference between a t-test and a F-test in a mixed model (anova vs summary in lmerTest)
Type III tests require correct coding for lower-order effects to be meaningful, specifically orthogonal contrasts. The R default contr.treatment is not orthogonal, other contrasts are though (e.g., co |
42,319 | What does alignment between input and output mean for recurrent neural network | What the paragraph from Sutskever and Sutskever means is, in a single RNN, the RNN receives a sequence of inputs, and gives a sequence of outputs , typically the same number of outputs as inputs, like:
Inputs: i1 i2 i3 i4 i5 i6 i7
Outputs: o1 o2 o3 o4 o5 o6 o7
So, for each input, we get an output, which could be f... | What does alignment between input and output mean for recurrent neural network | What the paragraph from Sutskever and Sutskever means is, in a single RNN, the RNN receives a sequence of inputs, and gives a sequence of outputs , typically the same number of outputs as inputs, like | What does alignment between input and output mean for recurrent neural network
What the paragraph from Sutskever and Sutskever means is, in a single RNN, the RNN receives a sequence of inputs, and gives a sequence of outputs , typically the same number of outputs as inputs, like:
Inputs: i1 i2 i3 i4 i5 i6 i7
Outputs: ... | What does alignment between input and output mean for recurrent neural network
What the paragraph from Sutskever and Sutskever means is, in a single RNN, the RNN receives a sequence of inputs, and gives a sequence of outputs , typically the same number of outputs as inputs, like |
42,320 | Is the expectation of the sufficient statistics $S(X)$ transverse the whole space in an exponential family? | Short answer to OP
Not necessarily, it depends on whether the extended convex cone spanned by varying the $\lambda_i$ outside the range of your parameter space can cover the whole mean parameter space. One situation is that your exponential model is over-parameterized, where you cannot "fill" the whole space by varying... | Is the expectation of the sufficient statistics $S(X)$ transverse the whole space in an exponential | Short answer to OP
Not necessarily, it depends on whether the extended convex cone spanned by varying the $\lambda_i$ outside the range of your parameter space can cover the whole mean parameter space | Is the expectation of the sufficient statistics $S(X)$ transverse the whole space in an exponential family?
Short answer to OP
Not necessarily, it depends on whether the extended convex cone spanned by varying the $\lambda_i$ outside the range of your parameter space can cover the whole mean parameter space. One situat... | Is the expectation of the sufficient statistics $S(X)$ transverse the whole space in an exponential
Short answer to OP
Not necessarily, it depends on whether the extended convex cone spanned by varying the $\lambda_i$ outside the range of your parameter space can cover the whole mean parameter space |
42,321 | Standardizing quadratic variables in linear model [closed] | I was just looking at the same question and did some simple simulations to get the answer using a poisson glm. It turns out that both methods make the exact same predictions as using the unstandardized variables. The difference is that method 2 (reflected in "mod1" in the code below) gives the exact same z-scores and p... | Standardizing quadratic variables in linear model [closed] | I was just looking at the same question and did some simple simulations to get the answer using a poisson glm. It turns out that both methods make the exact same predictions as using the unstandardize | Standardizing quadratic variables in linear model [closed]
I was just looking at the same question and did some simple simulations to get the answer using a poisson glm. It turns out that both methods make the exact same predictions as using the unstandardized variables. The difference is that method 2 (reflected in "m... | Standardizing quadratic variables in linear model [closed]
I was just looking at the same question and did some simple simulations to get the answer using a poisson glm. It turns out that both methods make the exact same predictions as using the unstandardize |
42,322 | Standardizing quadratic variables in linear model [closed] | One add-on to Neerajs and Andrews answer:
If you consider the correlations between the linear and squared term, you will see that method 2 (first squaring then scaling) produces the same correlation as the original variables. Method 1 on the other hand creates much more orthogonal variables:
vege <- sort(runif(200, 0, ... | Standardizing quadratic variables in linear model [closed] | One add-on to Neerajs and Andrews answer:
If you consider the correlations between the linear and squared term, you will see that method 2 (first squaring then scaling) produces the same correlation a | Standardizing quadratic variables in linear model [closed]
One add-on to Neerajs and Andrews answer:
If you consider the correlations between the linear and squared term, you will see that method 2 (first squaring then scaling) produces the same correlation as the original variables. Method 1 on the other hand creates ... | Standardizing quadratic variables in linear model [closed]
One add-on to Neerajs and Andrews answer:
If you consider the correlations between the linear and squared term, you will see that method 2 (first squaring then scaling) produces the same correlation a |
42,323 | Standardizing quadratic variables in linear model [closed] | I would prefer the first method. Here you have normalized the data initially itself before taking the linear / quadratic terms in your equation.
Standardization is meant for data (predictor) to bring them into same scale.
It is not related with any terms. You can go to cubic/quadratic terms too here in your equation ba... | Standardizing quadratic variables in linear model [closed] | I would prefer the first method. Here you have normalized the data initially itself before taking the linear / quadratic terms in your equation.
Standardization is meant for data (predictor) to bring | Standardizing quadratic variables in linear model [closed]
I would prefer the first method. Here you have normalized the data initially itself before taking the linear / quadratic terms in your equation.
Standardization is meant for data (predictor) to bring them into same scale.
It is not related with any terms. You c... | Standardizing quadratic variables in linear model [closed]
I would prefer the first method. Here you have normalized the data initially itself before taking the linear / quadratic terms in your equation.
Standardization is meant for data (predictor) to bring |
42,324 | What is the difference between artificial intelligence and machine intelligence? | Nice, provocative, definition is given by Yonatan Zunger in his Asking the Right Questions About AI article posted on Medium.com:
... I’m going to use the terms “artificial intelligence”
(AI) and “machine learning” (ML) more or less interchangeably. There’s
a stupid reason these terms mean almost the same thing: i... | What is the difference between artificial intelligence and machine intelligence? | Nice, provocative, definition is given by Yonatan Zunger in his Asking the Right Questions About AI article posted on Medium.com:
... I’m going to use the terms “artificial intelligence”
(AI) and “ | What is the difference between artificial intelligence and machine intelligence?
Nice, provocative, definition is given by Yonatan Zunger in his Asking the Right Questions About AI article posted on Medium.com:
... I’m going to use the terms “artificial intelligence”
(AI) and “machine learning” (ML) more or less int... | What is the difference between artificial intelligence and machine intelligence?
Nice, provocative, definition is given by Yonatan Zunger in his Asking the Right Questions About AI article posted on Medium.com:
... I’m going to use the terms “artificial intelligence”
(AI) and “ |
42,325 | What is the difference between artificial intelligence and machine intelligence? | Artificial intelligence (AI) in their highly cited book by that name has been described by Stuart J. Russell and Peter Norvig as intelligent agent design consisting of the following:
The unifying theme of the book is the concept of an intelligent agent.
In this view, the problem of AI is to describe and build agents... | What is the difference between artificial intelligence and machine intelligence? | Artificial intelligence (AI) in their highly cited book by that name has been described by Stuart J. Russell and Peter Norvig as intelligent agent design consisting of the following:
The unifying the | What is the difference between artificial intelligence and machine intelligence?
Artificial intelligence (AI) in their highly cited book by that name has been described by Stuart J. Russell and Peter Norvig as intelligent agent design consisting of the following:
The unifying theme of the book is the concept of an int... | What is the difference between artificial intelligence and machine intelligence?
Artificial intelligence (AI) in their highly cited book by that name has been described by Stuart J. Russell and Peter Norvig as intelligent agent design consisting of the following:
The unifying the |
42,326 | What is the difference between artificial intelligence and machine intelligence? | Difference between AI & ML
Artificial Intelligence : The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non natural thing and Intelligence means ability to understand or think. There is a misconception that Artificial Intelli... | What is the difference between artificial intelligence and machine intelligence? | Difference between AI & ML
Artificial Intelligence : The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or n | What is the difference between artificial intelligence and machine intelligence?
Difference between AI & ML
Artificial Intelligence : The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non natural thing and Intelligence means... | What is the difference between artificial intelligence and machine intelligence?
Difference between AI & ML
Artificial Intelligence : The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or n |
42,327 | Judging flatness of time-series | I would consider the following protocol, which I would call quick-and-dirty. Code is from R.
a) Determine a linear model
mod<-lm(signal ~ t)
to see if there is evidence for a trend. See if the coefficients have p-values satisfying $p \le 0.025$, or some other suitably small $\alpha/2$.
b) Subtract the model elemen... | Judging flatness of time-series | I would consider the following protocol, which I would call quick-and-dirty. Code is from R.
a) Determine a linear model
mod<-lm(signal ~ t)
to see if there is evidence for a trend. See if the co | Judging flatness of time-series
I would consider the following protocol, which I would call quick-and-dirty. Code is from R.
a) Determine a linear model
mod<-lm(signal ~ t)
to see if there is evidence for a trend. See if the coefficients have p-values satisfying $p \le 0.025$, or some other suitably small $\alpha/... | Judging flatness of time-series
I would consider the following protocol, which I would call quick-and-dirty. Code is from R.
a) Determine a linear model
mod<-lm(signal ~ t)
to see if there is evidence for a trend. See if the co |
42,328 | Judging flatness of time-series | I can imagine a report that summarizes statistically significant model structures for each time series. This report would contain information like the #of step/level shifts encountered, the # of positive pulses , the # of negative pulses , the # of deterministic trends , a pointer indicating a stochastic trend ,# of br... | Judging flatness of time-series | I can imagine a report that summarizes statistically significant model structures for each time series. This report would contain information like the #of step/level shifts encountered, the # of posit | Judging flatness of time-series
I can imagine a report that summarizes statistically significant model structures for each time series. This report would contain information like the #of step/level shifts encountered, the # of positive pulses , the # of negative pulses , the # of deterministic trends , a pointer indica... | Judging flatness of time-series
I can imagine a report that summarizes statistically significant model structures for each time series. This report would contain information like the #of step/level shifts encountered, the # of posit |
42,329 | How to decide the "best" accuracy score for prediction of binary outcome? | The scores are continuous not (necessarily) in the sense of small perturbations to the input data, but rather in small perturbations of the prediction model. Of course, small perturbations in input data will often yield small perturbations in the model.
If you have probabilistic predictions for a discrete classificatio... | How to decide the "best" accuracy score for prediction of binary outcome? | The scores are continuous not (necessarily) in the sense of small perturbations to the input data, but rather in small perturbations of the prediction model. Of course, small perturbations in input da | How to decide the "best" accuracy score for prediction of binary outcome?
The scores are continuous not (necessarily) in the sense of small perturbations to the input data, but rather in small perturbations of the prediction model. Of course, small perturbations in input data will often yield small perturbations in the... | How to decide the "best" accuracy score for prediction of binary outcome?
The scores are continuous not (necessarily) in the sense of small perturbations to the input data, but rather in small perturbations of the prediction model. Of course, small perturbations in input da |
42,330 | Famous historical example(s) of Type II error | "The harm done by tests of significance" (pdf) relates 3 true stories in which not rejecting the null while it was actually false (so making a Type II error) has been interpreted as accepting the null and had lead to bad general interest decisions. Here is a brief summary of one of them:
The practice of allowing righ... | Famous historical example(s) of Type II error | "The harm done by tests of significance" (pdf) relates 3 true stories in which not rejecting the null while it was actually false (so making a Type II error) has been interpreted as accepting the nul | Famous historical example(s) of Type II error
"The harm done by tests of significance" (pdf) relates 3 true stories in which not rejecting the null while it was actually false (so making a Type II error) has been interpreted as accepting the null and had lead to bad general interest decisions. Here is a brief summary ... | Famous historical example(s) of Type II error
"The harm done by tests of significance" (pdf) relates 3 true stories in which not rejecting the null while it was actually false (so making a Type II error) has been interpreted as accepting the nul |
42,331 | How do I examine biasedness in nonlinear regression? | If you define
$$z\equiv{y}-e^{\beta_2 x_2}=\beta_0+\beta_1 x_1+u$$
then given $\beta_2$, you can estimate $\beta_0$ and $\beta_1$ via OLS.
This trick is useful computationally to turn your problem into a scalar nonlinear optimization over $\beta_2$.
As OLS is unbiased, any bias in the nonlinear parameter will be inher... | How do I examine biasedness in nonlinear regression? | If you define
$$z\equiv{y}-e^{\beta_2 x_2}=\beta_0+\beta_1 x_1+u$$
then given $\beta_2$, you can estimate $\beta_0$ and $\beta_1$ via OLS.
This trick is useful computationally to turn your problem int | How do I examine biasedness in nonlinear regression?
If you define
$$z\equiv{y}-e^{\beta_2 x_2}=\beta_0+\beta_1 x_1+u$$
then given $\beta_2$, you can estimate $\beta_0$ and $\beta_1$ via OLS.
This trick is useful computationally to turn your problem into a scalar nonlinear optimization over $\beta_2$.
As OLS is unbiase... | How do I examine biasedness in nonlinear regression?
If you define
$$z\equiv{y}-e^{\beta_2 x_2}=\beta_0+\beta_1 x_1+u$$
then given $\beta_2$, you can estimate $\beta_0$ and $\beta_1$ via OLS.
This trick is useful computationally to turn your problem int |
42,332 | How do I examine biasedness in nonlinear regression? | In order to be able to talk about whether $\hat{\beta}$ is biased, we need to have some concept of a true $\beta$. That is, we need to specify a data-generating process. So in order to make this problem tractable, let's assume that
$y_i = \beta_0 + \beta_1 x_{i1} + e^{\beta_2 x_{i2}} + u_i$
and
$u_i \sim N(0, \sigma^2)... | How do I examine biasedness in nonlinear regression? | In order to be able to talk about whether $\hat{\beta}$ is biased, we need to have some concept of a true $\beta$. That is, we need to specify a data-generating process. So in order to make this probl | How do I examine biasedness in nonlinear regression?
In order to be able to talk about whether $\hat{\beta}$ is biased, we need to have some concept of a true $\beta$. That is, we need to specify a data-generating process. So in order to make this problem tractable, let's assume that
$y_i = \beta_0 + \beta_1 x_{i1} + e... | How do I examine biasedness in nonlinear regression?
In order to be able to talk about whether $\hat{\beta}$ is biased, we need to have some concept of a true $\beta$. That is, we need to specify a data-generating process. So in order to make this probl |
42,333 | Ensemble learning with time-varying covariates and effects | The most widely used approach for survival analysis of time-varying covariates (TVCs) are Cox models. There you need to create a dataset with TVCs which is described in this vignette
fit.cox <- coxph(Surv(time1, time2, event) ~ x), data = data)
However there is also the possibility to include TVCs in random survival f... | Ensemble learning with time-varying covariates and effects | The most widely used approach for survival analysis of time-varying covariates (TVCs) are Cox models. There you need to create a dataset with TVCs which is described in this vignette
fit.cox <- coxph( | Ensemble learning with time-varying covariates and effects
The most widely used approach for survival analysis of time-varying covariates (TVCs) are Cox models. There you need to create a dataset with TVCs which is described in this vignette
fit.cox <- coxph(Surv(time1, time2, event) ~ x), data = data)
However there i... | Ensemble learning with time-varying covariates and effects
The most widely used approach for survival analysis of time-varying covariates (TVCs) are Cox models. There you need to create a dataset with TVCs which is described in this vignette
fit.cox <- coxph( |
42,334 | Intuitively how does Bayesian Network Structure Learning Work? | There is a concept in Bayesian network literature, called I-equivalence. Two Bayesian network structures are called I-equivalence if they encode the same set of conditional independencies. For example, the following three structures are I-equivalence, since they all encode $A$ is independent of $C$ given $B$:
$$A\right... | Intuitively how does Bayesian Network Structure Learning Work? | There is a concept in Bayesian network literature, called I-equivalence. Two Bayesian network structures are called I-equivalence if they encode the same set of conditional independencies. For example | Intuitively how does Bayesian Network Structure Learning Work?
There is a concept in Bayesian network literature, called I-equivalence. Two Bayesian network structures are called I-equivalence if they encode the same set of conditional independencies. For example, the following three structures are I-equivalence, since... | Intuitively how does Bayesian Network Structure Learning Work?
There is a concept in Bayesian network literature, called I-equivalence. Two Bayesian network structures are called I-equivalence if they encode the same set of conditional independencies. For example |
42,335 | Intuitively how does Bayesian Network Structure Learning Work? | You're right Bayesian Networks don't hold any information about real causality, it just assumes one random variable directly influences another random variable , and the joint distribution of those variables tells us the second variable also directly influences the first one. They are just two Mathematical points of vi... | Intuitively how does Bayesian Network Structure Learning Work? | You're right Bayesian Networks don't hold any information about real causality, it just assumes one random variable directly influences another random variable , and the joint distribution of those va | Intuitively how does Bayesian Network Structure Learning Work?
You're right Bayesian Networks don't hold any information about real causality, it just assumes one random variable directly influences another random variable , and the joint distribution of those variables tells us the second variable also directly influe... | Intuitively how does Bayesian Network Structure Learning Work?
You're right Bayesian Networks don't hold any information about real causality, it just assumes one random variable directly influences another random variable , and the joint distribution of those va |
42,336 | How to say if the variable is significant looking only at "t value"? | Beforehand: The t distribution changes its shape depending on the degrees of freedom (df). Since in regression $df= n - p - 1$ where $n$ is the sample size and $p$ the number of the predictors, we know that $df$ increases if $n$ does (assuming $p$ stays the same). The important thing here is that the bigger the df the ... | How to say if the variable is significant looking only at "t value"? | Beforehand: The t distribution changes its shape depending on the degrees of freedom (df). Since in regression $df= n - p - 1$ where $n$ is the sample size and $p$ the number of the predictors, we kno | How to say if the variable is significant looking only at "t value"?
Beforehand: The t distribution changes its shape depending on the degrees of freedom (df). Since in regression $df= n - p - 1$ where $n$ is the sample size and $p$ the number of the predictors, we know that $df$ increases if $n$ does (assuming $p$ sta... | How to say if the variable is significant looking only at "t value"?
Beforehand: The t distribution changes its shape depending on the degrees of freedom (df). Since in regression $df= n - p - 1$ where $n$ is the sample size and $p$ the number of the predictors, we kno |
42,337 | How to say if the variable is significant looking only at "t value"? | When dealing with significance testing, you are deciding between two hypotheses
$H_0:\beta=0$
$H_1:\beta\neq0$
A beta coefficient is significant if you are able to reject the null hypothesis for a specified level of significance $\alpha$, that is usually chosen to be $0.05$.
If you use the p-value, you reject the n... | How to say if the variable is significant looking only at "t value"? | When dealing with significance testing, you are deciding between two hypotheses
$H_0:\beta=0$
$H_1:\beta\neq0$
A beta coefficient is significant if you are able to reject the null hypothesis for a | How to say if the variable is significant looking only at "t value"?
When dealing with significance testing, you are deciding between two hypotheses
$H_0:\beta=0$
$H_1:\beta\neq0$
A beta coefficient is significant if you are able to reject the null hypothesis for a specified level of significance $\alpha$, that is u... | How to say if the variable is significant looking only at "t value"?
When dealing with significance testing, you are deciding between two hypotheses
$H_0:\beta=0$
$H_1:\beta\neq0$
A beta coefficient is significant if you are able to reject the null hypothesis for a |
42,338 | How to find the prior distribution in De Finetti Representation Theorem? | De Finetti's theorem proves the existence of a prior distribution $H(\theta)$ such that $$
\int^1_0\theta\,^k(1-\theta)^{n-k}dH(\theta)=\frac{\Gamma(r+w)}{\Gamma(r)\Gamma(w)}\frac{\Gamma(r+k)\Gamma(w+n-k)}{\Gamma(r+w+n)}
$$ Looking more closely at the proof of this theorem, it seems that the prior distribution is obtai... | How to find the prior distribution in De Finetti Representation Theorem? | De Finetti's theorem proves the existence of a prior distribution $H(\theta)$ such that $$
\int^1_0\theta\,^k(1-\theta)^{n-k}dH(\theta)=\frac{\Gamma(r+w)}{\Gamma(r)\Gamma(w)}\frac{\Gamma(r+k)\Gamma(w+ | How to find the prior distribution in De Finetti Representation Theorem?
De Finetti's theorem proves the existence of a prior distribution $H(\theta)$ such that $$
\int^1_0\theta\,^k(1-\theta)^{n-k}dH(\theta)=\frac{\Gamma(r+w)}{\Gamma(r)\Gamma(w)}\frac{\Gamma(r+k)\Gamma(w+n-k)}{\Gamma(r+w+n)}
$$ Looking more closely at... | How to find the prior distribution in De Finetti Representation Theorem?
De Finetti's theorem proves the existence of a prior distribution $H(\theta)$ such that $$
\int^1_0\theta\,^k(1-\theta)^{n-k}dH(\theta)=\frac{\Gamma(r+w)}{\Gamma(r)\Gamma(w)}\frac{\Gamma(r+k)\Gamma(w+ |
42,339 | Optimization by random sampling | It sound like you are interested in studying stochastic optimisation methods. If you re-frame optimisation as a type of sampling problem, you will obtain a stochastic optimisation method, and this latter method will only be advantageous if it provides some improvement over analogous deterministic optimisation methods.... | Optimization by random sampling | It sound like you are interested in studying stochastic optimisation methods. If you re-frame optimisation as a type of sampling problem, you will obtain a stochastic optimisation method, and this la | Optimization by random sampling
It sound like you are interested in studying stochastic optimisation methods. If you re-frame optimisation as a type of sampling problem, you will obtain a stochastic optimisation method, and this latter method will only be advantageous if it provides some improvement over analogous det... | Optimization by random sampling
It sound like you are interested in studying stochastic optimisation methods. If you re-frame optimisation as a type of sampling problem, you will obtain a stochastic optimisation method, and this la |
42,340 | How to read the x-axis of this qqplot? | This seems to be a qqplot of the data compared with a standard normal distribution, so I would have thought the $x$ values should the typical values of the population quantiles of a standard normal distribution
So with $105$ observations I would have thought the extreme left $x$ value should be not far away from $\Phi^... | How to read the x-axis of this qqplot? | This seems to be a qqplot of the data compared with a standard normal distribution, so I would have thought the $x$ values should the typical values of the population quantiles of a standard normal di | How to read the x-axis of this qqplot?
This seems to be a qqplot of the data compared with a standard normal distribution, so I would have thought the $x$ values should the typical values of the population quantiles of a standard normal distribution
So with $105$ observations I would have thought the extreme left $x$ v... | How to read the x-axis of this qqplot?
This seems to be a qqplot of the data compared with a standard normal distribution, so I would have thought the $x$ values should the typical values of the population quantiles of a standard normal di |
42,341 | Plotting Correlation Matrix over time | There are a number of issues here (and whether you use ggplot2 strikes me as entirely orthogonal to them). First, recognize that that correlations don't necessarily scale in an intuitive, 'linear' manner (in large part because their possible range is bounded). It's worth thinking about how you want to represent the v... | Plotting Correlation Matrix over time | There are a number of issues here (and whether you use ggplot2 strikes me as entirely orthogonal to them). First, recognize that that correlations don't necessarily scale in an intuitive, 'linear' ma | Plotting Correlation Matrix over time
There are a number of issues here (and whether you use ggplot2 strikes me as entirely orthogonal to them). First, recognize that that correlations don't necessarily scale in an intuitive, 'linear' manner (in large part because their possible range is bounded). It's worth thinking... | Plotting Correlation Matrix over time
There are a number of issues here (and whether you use ggplot2 strikes me as entirely orthogonal to them). First, recognize that that correlations don't necessarily scale in an intuitive, 'linear' ma |
42,342 | Plotting Correlation Matrix over time | In my opinion it is better to quantify the effect of changes in parameters ( a proxy for local correlations ) rather than attempt a visual as attempts at visual comparisons can be quite subjective Your question is similar to "How do I test if the parameters of my model change over time" . What I have done is to program... | Plotting Correlation Matrix over time | In my opinion it is better to quantify the effect of changes in parameters ( a proxy for local correlations ) rather than attempt a visual as attempts at visual comparisons can be quite subjective You | Plotting Correlation Matrix over time
In my opinion it is better to quantify the effect of changes in parameters ( a proxy for local correlations ) rather than attempt a visual as attempts at visual comparisons can be quite subjective Your question is similar to "How do I test if the parameters of my model change over ... | Plotting Correlation Matrix over time
In my opinion it is better to quantify the effect of changes in parameters ( a proxy for local correlations ) rather than attempt a visual as attempts at visual comparisons can be quite subjective You |
42,343 | What is the R rnbinom negative binomial dispersion parameter? | The documentation calls it "size":
size target for number of successful trials, or dispersion parameter (the shape parameter of the gamma mixing distribution). Must be strictly positive, need not be integer.
That is the simplest way to understand it. The negative binomial distribution is typically understood as:... | What is the R rnbinom negative binomial dispersion parameter? | The documentation calls it "size":
size target for number of successful trials, or dispersion parameter (the shape parameter of the gamma mixing distribution). Must be strictly positive, need not | What is the R rnbinom negative binomial dispersion parameter?
The documentation calls it "size":
size target for number of successful trials, or dispersion parameter (the shape parameter of the gamma mixing distribution). Must be strictly positive, need not be integer.
That is the simplest way to understand it. ... | What is the R rnbinom negative binomial dispersion parameter?
The documentation calls it "size":
size target for number of successful trials, or dispersion parameter (the shape parameter of the gamma mixing distribution). Must be strictly positive, need not |
42,344 | What is the R rnbinom negative binomial dispersion parameter? | I have recently just had trouble with this question, and as I found no concrete answer on the edit of the original post I will share my findings.
The glm.nb function estimates a dispersion parameter, labelled $\theta$. This is often called the size parameter and notated by $r$, as shown in the Negative Binomial Wikiped... | What is the R rnbinom negative binomial dispersion parameter? | I have recently just had trouble with this question, and as I found no concrete answer on the edit of the original post I will share my findings.
The glm.nb function estimates a dispersion parameter, | What is the R rnbinom negative binomial dispersion parameter?
I have recently just had trouble with this question, and as I found no concrete answer on the edit of the original post I will share my findings.
The glm.nb function estimates a dispersion parameter, labelled $\theta$. This is often called the size parameter... | What is the R rnbinom negative binomial dispersion parameter?
I have recently just had trouble with this question, and as I found no concrete answer on the edit of the original post I will share my findings.
The glm.nb function estimates a dispersion parameter, |
42,345 | Information geometry tutorial | You can give a try to:
Information Geometry and Its Applications: An Overview
Pattern learning and recognition on statistical manifolds: An information-geometric review | Information geometry tutorial | You can give a try to:
Information Geometry and Its Applications: An Overview
Pattern learning and recognition on statistical manifolds: An information-geometric review | Information geometry tutorial
You can give a try to:
Information Geometry and Its Applications: An Overview
Pattern learning and recognition on statistical manifolds: An information-geometric review | Information geometry tutorial
You can give a try to:
Information Geometry and Its Applications: An Overview
Pattern learning and recognition on statistical manifolds: An information-geometric review |
42,346 | R glmnet and elasticnet gives different results, why? | x=model.matrix(Apps~.,College)[,-1]
y=College$Apps
enet.model=enet(x, y, lambda=1)
mx_x <- data.frame(coef_enet = round(predict(enet.model, type="coef", s=2, mode="penalty", naive = F)$coefficients, 2))
mx_x$coef_enet.naive <- round(predict(enet.model, type="coef", s=2, mode="penalty", naive = T)$coefficients, 2)
glm... | R glmnet and elasticnet gives different results, why? | x=model.matrix(Apps~.,College)[,-1]
y=College$Apps
enet.model=enet(x, y, lambda=1)
mx_x <- data.frame(coef_enet = round(predict(enet.model, type="coef", s=2, mode="penalty", naive = F)$coefficients, | R glmnet and elasticnet gives different results, why?
x=model.matrix(Apps~.,College)[,-1]
y=College$Apps
enet.model=enet(x, y, lambda=1)
mx_x <- data.frame(coef_enet = round(predict(enet.model, type="coef", s=2, mode="penalty", naive = F)$coefficients, 2))
mx_x$coef_enet.naive <- round(predict(enet.model, type="coef",... | R glmnet and elasticnet gives different results, why?
x=model.matrix(Apps~.,College)[,-1]
y=College$Apps
enet.model=enet(x, y, lambda=1)
mx_x <- data.frame(coef_enet = round(predict(enet.model, type="coef", s=2, mode="penalty", naive = F)$coefficients, |
42,347 | What is the problem with training Neural Networks with back propagation with activation functions that only output positive values? [duplicate] | The gradient of sigmoid function is $$ (1-\sigma) \sigma $$ So if the input is $[0,1]$ then the output of the gradients on $w$ could only be all positive or all negative. You can check more on the notebook.
Sigmoid outputs are not zero-centered. This is undesirable since neurons in later layers of processing in a Neur... | What is the problem with training Neural Networks with back propagation with activation functions th | The gradient of sigmoid function is $$ (1-\sigma) \sigma $$ So if the input is $[0,1]$ then the output of the gradients on $w$ could only be all positive or all negative. You can check more on the not | What is the problem with training Neural Networks with back propagation with activation functions that only output positive values? [duplicate]
The gradient of sigmoid function is $$ (1-\sigma) \sigma $$ So if the input is $[0,1]$ then the output of the gradients on $w$ could only be all positive or all negative. You c... | What is the problem with training Neural Networks with back propagation with activation functions th
The gradient of sigmoid function is $$ (1-\sigma) \sigma $$ So if the input is $[0,1]$ then the output of the gradients on $w$ could only be all positive or all negative. You can check more on the not |
42,348 | What is the problem with training Neural Networks with back propagation with activation functions that only output positive values? [duplicate] | $$f = w^T.x + b$$
Thus, the gradient is: $$\frac{\partial f}{\partial w} = x $$.
If $x > 0$, then this expression is positive.
$$\sigma = \frac{1}{1+ \exp(-f) }$$
$$\frac{\partial \sigma}{\partial f} = \sigma (1 - \sigma)$$
Now since, $\sigma$ is sigmoid function and it always outputs in the range (0,1) this expressio... | What is the problem with training Neural Networks with back propagation with activation functions th | $$f = w^T.x + b$$
Thus, the gradient is: $$\frac{\partial f}{\partial w} = x $$.
If $x > 0$, then this expression is positive.
$$\sigma = \frac{1}{1+ \exp(-f) }$$
$$\frac{\partial \sigma}{\partial f} | What is the problem with training Neural Networks with back propagation with activation functions that only output positive values? [duplicate]
$$f = w^T.x + b$$
Thus, the gradient is: $$\frac{\partial f}{\partial w} = x $$.
If $x > 0$, then this expression is positive.
$$\sigma = \frac{1}{1+ \exp(-f) }$$
$$\frac{\par... | What is the problem with training Neural Networks with back propagation with activation functions th
$$f = w^T.x + b$$
Thus, the gradient is: $$\frac{\partial f}{\partial w} = x $$.
If $x > 0$, then this expression is positive.
$$\sigma = \frac{1}{1+ \exp(-f) }$$
$$\frac{\partial \sigma}{\partial f} |
42,349 | Does it take progressively fewer EXTRA correct answers to move up a grading curve in standardised exams? | Some understanding of the why can be gleaned from a simple but realistic model.
The curve shown in the question is consistent with a 46-question test in which each question contributes $100/46 \approx 2$ to the total score when answered correctly and otherwise contributes nothing. It is "consistent" in the sense that ... | Does it take progressively fewer EXTRA correct answers to move up a grading curve in standardised ex | Some understanding of the why can be gleaned from a simple but realistic model.
The curve shown in the question is consistent with a 46-question test in which each question contributes $100/46 \approx | Does it take progressively fewer EXTRA correct answers to move up a grading curve in standardised exams?
Some understanding of the why can be gleaned from a simple but realistic model.
The curve shown in the question is consistent with a 46-question test in which each question contributes $100/46 \approx 2$ to the tota... | Does it take progressively fewer EXTRA correct answers to move up a grading curve in standardised ex
Some understanding of the why can be gleaned from a simple but realistic model.
The curve shown in the question is consistent with a 46-question test in which each question contributes $100/46 \approx |
42,350 | The distribution for the Kronecker product of two uniform random vectors in unit sphere? | Yes. This becomes clear upon working through the definitions.
The Kronecker Product
The unit sphere $S^{n-1}$ is the set of unit vectors in the Euclidean space $E^n = \left(\mathbb{R}^n, ||\cdot ||\right)$ where $$||(x_1,x_2,\ldots,x_n)^\prime|| = \sqrt{x_1^2 + x_2^2 + \cdots + x_n^2}$$ is the Euclidean norm.
The "Kro... | The distribution for the Kronecker product of two uniform random vectors in unit sphere? | Yes. This becomes clear upon working through the definitions.
The Kronecker Product
The unit sphere $S^{n-1}$ is the set of unit vectors in the Euclidean space $E^n = \left(\mathbb{R}^n, ||\cdot ||\r | The distribution for the Kronecker product of two uniform random vectors in unit sphere?
Yes. This becomes clear upon working through the definitions.
The Kronecker Product
The unit sphere $S^{n-1}$ is the set of unit vectors in the Euclidean space $E^n = \left(\mathbb{R}^n, ||\cdot ||\right)$ where $$||(x_1,x_2,\ldot... | The distribution for the Kronecker product of two uniform random vectors in unit sphere?
Yes. This becomes clear upon working through the definitions.
The Kronecker Product
The unit sphere $S^{n-1}$ is the set of unit vectors in the Euclidean space $E^n = \left(\mathbb{R}^n, ||\cdot ||\r |
42,351 | The distribution for the Kronecker product of two uniform random vectors in unit sphere? | As long as $x$ and $y$ are independent, then what you say is true.
It's easiest to think about angles rather than points in space.
For example, in the 2-D sphere in $\mathbb R^3$, we can generate points uniformly on the surface of that sphere by sampling $\theta_1$ and $\theta_2$ independently from $U[0,2\pi]$ and taki... | The distribution for the Kronecker product of two uniform random vectors in unit sphere? | As long as $x$ and $y$ are independent, then what you say is true.
It's easiest to think about angles rather than points in space.
For example, in the 2-D sphere in $\mathbb R^3$, we can generate poin | The distribution for the Kronecker product of two uniform random vectors in unit sphere?
As long as $x$ and $y$ are independent, then what you say is true.
It's easiest to think about angles rather than points in space.
For example, in the 2-D sphere in $\mathbb R^3$, we can generate points uniformly on the surface of ... | The distribution for the Kronecker product of two uniform random vectors in unit sphere?
As long as $x$ and $y$ are independent, then what you say is true.
It's easiest to think about angles rather than points in space.
For example, in the 2-D sphere in $\mathbb R^3$, we can generate poin |
42,352 | Linear regression with several DVs with correlated errors | This is in fact called multivariate regression. I just think that it's not a commonly-used term because it's not a commonly-used model. Note also that it is very easy to confuse with "multiple linear regression."
The reason why multivariate regression is so relatively unpopular (and not implemented explicitly in any ma... | Linear regression with several DVs with correlated errors | This is in fact called multivariate regression. I just think that it's not a commonly-used term because it's not a commonly-used model. Note also that it is very easy to confuse with "multiple linear | Linear regression with several DVs with correlated errors
This is in fact called multivariate regression. I just think that it's not a commonly-used term because it's not a commonly-used model. Note also that it is very easy to confuse with "multiple linear regression."
The reason why multivariate regression is so rela... | Linear regression with several DVs with correlated errors
This is in fact called multivariate regression. I just think that it's not a commonly-used term because it's not a commonly-used model. Note also that it is very easy to confuse with "multiple linear |
42,353 | GLMM - between, within and nested | I don't see why Rating is nested within ID. This means that every every unique Rating belongs to one and only one ID which does not appear to be the case. If Rating is to be treated as random, then these should be crossed random effects. See the answer here for the difference between crossed and nested random effects a... | GLMM - between, within and nested | I don't see why Rating is nested within ID. This means that every every unique Rating belongs to one and only one ID which does not appear to be the case. If Rating is to be treated as random, then th | GLMM - between, within and nested
I don't see why Rating is nested within ID. This means that every every unique Rating belongs to one and only one ID which does not appear to be the case. If Rating is to be treated as random, then these should be crossed random effects. See the answer here for the difference between c... | GLMM - between, within and nested
I don't see why Rating is nested within ID. This means that every every unique Rating belongs to one and only one ID which does not appear to be the case. If Rating is to be treated as random, then th |
42,354 | Poisson random variable self-study question | To check (1), note that the expected number of people arriving after you must equal the expected number of people arriving before you and that the sum of those is the expected number of people (besides yourself), which the problem states is equal to 10. Therefore the answer must be $10/2=5$.
For (2), use the hint you p... | Poisson random variable self-study question | To check (1), note that the expected number of people arriving after you must equal the expected number of people arriving before you and that the sum of those is the expected number of people (beside | Poisson random variable self-study question
To check (1), note that the expected number of people arriving after you must equal the expected number of people arriving before you and that the sum of those is the expected number of people (besides yourself), which the problem states is equal to 10. Therefore the answer m... | Poisson random variable self-study question
To check (1), note that the expected number of people arriving after you must equal the expected number of people arriving before you and that the sum of those is the expected number of people (beside |
42,355 | Poisson random variable self-study question | This is not a full answer, but may help you get there.
Say $N$ is the total number of people invited to the party, and $n$ is your position in the arrival sequence. Then from your setup we have that $N-1$ is Poisson distributed with mean 10. So you can answer your questions in two steps: First, you can compute the answ... | Poisson random variable self-study question | This is not a full answer, but may help you get there.
Say $N$ is the total number of people invited to the party, and $n$ is your position in the arrival sequence. Then from your setup we have that $ | Poisson random variable self-study question
This is not a full answer, but may help you get there.
Say $N$ is the total number of people invited to the party, and $n$ is your position in the arrival sequence. Then from your setup we have that $N-1$ is Poisson distributed with mean 10. So you can answer your questions i... | Poisson random variable self-study question
This is not a full answer, but may help you get there.
Say $N$ is the total number of people invited to the party, and $n$ is your position in the arrival sequence. Then from your setup we have that $ |
42,356 | Poisson random variable self-study question | Here's my attempt. Let's assume that the number of invitees (excluding you) to the party, $N$, is Poisson distributed with parameter $\lambda$:
$$N\sim \text{POI}(\lambda)$$
Let's further assume that all those invited actually attend the party. Moreover, let the arrival time of you and all other party attendees be unif... | Poisson random variable self-study question | Here's my attempt. Let's assume that the number of invitees (excluding you) to the party, $N$, is Poisson distributed with parameter $\lambda$:
$$N\sim \text{POI}(\lambda)$$
Let's further assume that | Poisson random variable self-study question
Here's my attempt. Let's assume that the number of invitees (excluding you) to the party, $N$, is Poisson distributed with parameter $\lambda$:
$$N\sim \text{POI}(\lambda)$$
Let's further assume that all those invited actually attend the party. Moreover, let the arrival time ... | Poisson random variable self-study question
Here's my attempt. Let's assume that the number of invitees (excluding you) to the party, $N$, is Poisson distributed with parameter $\lambda$:
$$N\sim \text{POI}(\lambda)$$
Let's further assume that |
42,357 | Change threshold of classifier based on ROC | The Accuracy (or the area under the ROC curve) depends on the sample used to construct the ROC curve (see, e.g., How to interpret 95% confidence interval for Area Under Curve of ROC?). So if you use this ROC to optimise the threshold there is a risk of overfitting to the sample.
You can indeed "calibrate" a threshold... | Change threshold of classifier based on ROC | The Accuracy (or the area under the ROC curve) depends on the sample used to construct the ROC curve (see, e.g., How to interpret 95% confidence interval for Area Under Curve of ROC?). So if you use t | Change threshold of classifier based on ROC
The Accuracy (or the area under the ROC curve) depends on the sample used to construct the ROC curve (see, e.g., How to interpret 95% confidence interval for Area Under Curve of ROC?). So if you use this ROC to optimise the threshold there is a risk of overfitting to the samp... | Change threshold of classifier based on ROC
The Accuracy (or the area under the ROC curve) depends on the sample used to construct the ROC curve (see, e.g., How to interpret 95% confidence interval for Area Under Curve of ROC?). So if you use t |
42,358 | How can I prepare the input layer for recurrent neural network if there are many categorical variables? | The "default" way of dealing with categorical variables for neural networks is to use embeddings. The most popular usage is word embeddings, where words are represented by vector representation (learned or pre-trained). The advantages of such approach is that it has smaller dimensionality then if you used one-hot encod... | How can I prepare the input layer for recurrent neural network if there are many categorical variabl | The "default" way of dealing with categorical variables for neural networks is to use embeddings. The most popular usage is word embeddings, where words are represented by vector representation (learn | How can I prepare the input layer for recurrent neural network if there are many categorical variables?
The "default" way of dealing with categorical variables for neural networks is to use embeddings. The most popular usage is word embeddings, where words are represented by vector representation (learned or pre-traine... | How can I prepare the input layer for recurrent neural network if there are many categorical variabl
The "default" way of dealing with categorical variables for neural networks is to use embeddings. The most popular usage is word embeddings, where words are represented by vector representation (learn |
42,359 | How can I prepare the input layer for recurrent neural network if there are many categorical variables? | This is not my area of expertise, but my understanding is that it's a lot harder to learn on a sparse representation. You won't have many training examples for each input, so most of the neurons will be training on just a few examples.
The canonical example for RNNs would be NLP, and I think it's fairly standard to tr... | How can I prepare the input layer for recurrent neural network if there are many categorical variabl | This is not my area of expertise, but my understanding is that it's a lot harder to learn on a sparse representation. You won't have many training examples for each input, so most of the neurons will | How can I prepare the input layer for recurrent neural network if there are many categorical variables?
This is not my area of expertise, but my understanding is that it's a lot harder to learn on a sparse representation. You won't have many training examples for each input, so most of the neurons will be training on ... | How can I prepare the input layer for recurrent neural network if there are many categorical variabl
This is not my area of expertise, but my understanding is that it's a lot harder to learn on a sparse representation. You won't have many training examples for each input, so most of the neurons will |
42,360 | lavaan works with `cfa`, provides error with lavaan(method = 'cfa') | The cfa() function is a wrapper for lavaan, which (among other things) adds the arguments
auto.fix.first = TRUE
auto.var = TRUE
Changing your model type to CFA changes a few cosmetic things, but not the basic model. Because the lavaan() command has not put these in, you need to add the arguments, or add the appropria... | lavaan works with `cfa`, provides error with lavaan(method = 'cfa') | The cfa() function is a wrapper for lavaan, which (among other things) adds the arguments
auto.fix.first = TRUE
auto.var = TRUE
Changing your model type to CFA changes a few cosmetic things, but not | lavaan works with `cfa`, provides error with lavaan(method = 'cfa')
The cfa() function is a wrapper for lavaan, which (among other things) adds the arguments
auto.fix.first = TRUE
auto.var = TRUE
Changing your model type to CFA changes a few cosmetic things, but not the basic model. Because the lavaan() command has n... | lavaan works with `cfa`, provides error with lavaan(method = 'cfa')
The cfa() function is a wrapper for lavaan, which (among other things) adds the arguments
auto.fix.first = TRUE
auto.var = TRUE
Changing your model type to CFA changes a few cosmetic things, but not |
42,361 | Weighted arithmetic mean weight choice in a simplified Bayes estimator | I'm providing second answer since it is either: problem formulation that is unclear, or the answer provided by OP is wrong, since it does not address the problem. In my answer I'll try to refer to both of the cases.
First, let's try to define the problem. You have rankings of restaurants based on votes, where each vote... | Weighted arithmetic mean weight choice in a simplified Bayes estimator | I'm providing second answer since it is either: problem formulation that is unclear, or the answer provided by OP is wrong, since it does not address the problem. In my answer I'll try to refer to bot | Weighted arithmetic mean weight choice in a simplified Bayes estimator
I'm providing second answer since it is either: problem formulation that is unclear, or the answer provided by OP is wrong, since it does not address the problem. In my answer I'll try to refer to both of the cases.
First, let's try to define the pr... | Weighted arithmetic mean weight choice in a simplified Bayes estimator
I'm providing second answer since it is either: problem formulation that is unclear, or the answer provided by OP is wrong, since it does not address the problem. In my answer I'll try to refer to bot |
42,362 | Weighted arithmetic mean weight choice in a simplified Bayes estimator | The IMDB ratings example is a simple one, so you are right that in many cases they get more complicated.
Actually, the answer to your question is already given in the Wikipedia entry:
Note that $W$ is just the weighted arithmetic mean of $R$ and $C$ with
weight vector $(v, m)$. As the number of ratings surpasses $m$... | Weighted arithmetic mean weight choice in a simplified Bayes estimator | The IMDB ratings example is a simple one, so you are right that in many cases they get more complicated.
Actually, the answer to your question is already given in the Wikipedia entry:
Note that $W$ i | Weighted arithmetic mean weight choice in a simplified Bayes estimator
The IMDB ratings example is a simple one, so you are right that in many cases they get more complicated.
Actually, the answer to your question is already given in the Wikipedia entry:
Note that $W$ is just the weighted arithmetic mean of $R$ and $C... | Weighted arithmetic mean weight choice in a simplified Bayes estimator
The IMDB ratings example is a simple one, so you are right that in many cases they get more complicated.
Actually, the answer to your question is already given in the Wikipedia entry:
Note that $W$ i |
42,363 | Maximum entropy distribution of a proportion with known mean and variance? Is it a beta? | It's a truncated Normal distribution. This is a consequence of Boltzmann's Theorem.
The following analysis provides the details needed to implement a practical solution.
A Normal$(\mu,\sigma)$ distribution $F$ truncated to the interval $[0,1]$ arises by taking a standard Normal variable $X$ with probability distribut... | Maximum entropy distribution of a proportion with known mean and variance? Is it a beta? | It's a truncated Normal distribution. This is a consequence of Boltzmann's Theorem.
The following analysis provides the details needed to implement a practical solution.
A Normal$(\mu,\sigma)$ distr | Maximum entropy distribution of a proportion with known mean and variance? Is it a beta?
It's a truncated Normal distribution. This is a consequence of Boltzmann's Theorem.
The following analysis provides the details needed to implement a practical solution.
A Normal$(\mu,\sigma)$ distribution $F$ truncated to the in... | Maximum entropy distribution of a proportion with known mean and variance? Is it a beta?
It's a truncated Normal distribution. This is a consequence of Boltzmann's Theorem.
The following analysis provides the details needed to implement a practical solution.
A Normal$(\mu,\sigma)$ distr |
42,364 | Do Bayesians use the terminology "statistically significant"? | This issue doesn't come up often because when people use fully Bayesian methods, they don't usually create a null hypothesis and then make a choice on the basis of a hard threshold of relative probability, as you have. After all, a lot of the motivation for using Bayesian methods is to avoid such things.
I'd recommend ... | Do Bayesians use the terminology "statistically significant"? | This issue doesn't come up often because when people use fully Bayesian methods, they don't usually create a null hypothesis and then make a choice on the basis of a hard threshold of relative probabi | Do Bayesians use the terminology "statistically significant"?
This issue doesn't come up often because when people use fully Bayesian methods, they don't usually create a null hypothesis and then make a choice on the basis of a hard threshold of relative probability, as you have. After all, a lot of the motivation for ... | Do Bayesians use the terminology "statistically significant"?
This issue doesn't come up often because when people use fully Bayesian methods, they don't usually create a null hypothesis and then make a choice on the basis of a hard threshold of relative probabi |
42,365 | Do Bayesians use the terminology "statistically significant"? | Not really. Bayesian testing is very liberating for statisticians and non-statisticians. A distinction, and one which frequentists should consider, is the point of being rigorous in apriori specifying decision rules, or alternative hypotheses of interest. It allows one to report the results of a trial or study in terms... | Do Bayesians use the terminology "statistically significant"? | Not really. Bayesian testing is very liberating for statisticians and non-statisticians. A distinction, and one which frequentists should consider, is the point of being rigorous in apriori specifying | Do Bayesians use the terminology "statistically significant"?
Not really. Bayesian testing is very liberating for statisticians and non-statisticians. A distinction, and one which frequentists should consider, is the point of being rigorous in apriori specifying decision rules, or alternative hypotheses of interest. It... | Do Bayesians use the terminology "statistically significant"?
Not really. Bayesian testing is very liberating for statisticians and non-statisticians. A distinction, and one which frequentists should consider, is the point of being rigorous in apriori specifying |
42,366 | Is the Scheffé test of contrasts the "best-case" for post-hoc tests? | Firstly, there are many tests and each one has its good and bad sides over another test. Some of the most basic tests are Tukey's, Bonferroni's and Scheffés, so we can compare and contrast these to understand what goes into choosing a test.
A useful explanation of choosing between tests (http://www.itl.nist.gov/div898/... | Is the Scheffé test of contrasts the "best-case" for post-hoc tests? | Firstly, there are many tests and each one has its good and bad sides over another test. Some of the most basic tests are Tukey's, Bonferroni's and Scheffés, so we can compare and contrast these to un | Is the Scheffé test of contrasts the "best-case" for post-hoc tests?
Firstly, there are many tests and each one has its good and bad sides over another test. Some of the most basic tests are Tukey's, Bonferroni's and Scheffés, so we can compare and contrast these to understand what goes into choosing a test.
A useful e... | Is the Scheffé test of contrasts the "best-case" for post-hoc tests?
Firstly, there are many tests and each one has its good and bad sides over another test. Some of the most basic tests are Tukey's, Bonferroni's and Scheffés, so we can compare and contrast these to un |
42,367 | Relationship between Linear Projection and OLS Regression | Another formulation of the $\beta$ regression parameter estimator is as
$\hat{\beta} = \left(\mathbf{X}^T\mathbf{X}\right)^{-1} \mathbf{X}^T Y$
Here $\hat{\beta}$ is a two element vector of $\hat{\beta}_0$ the intercept and $\hat{\beta}_1$ the slope. I like to use $\mathbf{X}$ notationally as a design matrix with the ... | Relationship between Linear Projection and OLS Regression | Another formulation of the $\beta$ regression parameter estimator is as
$\hat{\beta} = \left(\mathbf{X}^T\mathbf{X}\right)^{-1} \mathbf{X}^T Y$
Here $\hat{\beta}$ is a two element vector of $\hat{\be | Relationship between Linear Projection and OLS Regression
Another formulation of the $\beta$ regression parameter estimator is as
$\hat{\beta} = \left(\mathbf{X}^T\mathbf{X}\right)^{-1} \mathbf{X}^T Y$
Here $\hat{\beta}$ is a two element vector of $\hat{\beta}_0$ the intercept and $\hat{\beta}_1$ the slope. I like to ... | Relationship between Linear Projection and OLS Regression
Another formulation of the $\beta$ regression parameter estimator is as
$\hat{\beta} = \left(\mathbf{X}^T\mathbf{X}\right)^{-1} \mathbf{X}^T Y$
Here $\hat{\beta}$ is a two element vector of $\hat{\be |
42,368 | Relationship between Linear Projection and OLS Regression | We start with the population model:
$$\begin{aligned}y &=E(y|{\bf x)}+e \\ E(e|{\bf x}) &=0 \end{aligned}$$
where $m({\bf x})=E(y|x)$ is the conditional expectation function (CEF) and e is the CEF error. It is important to note that the equations above are a definition. Here $E(e|{\bf x})=0$ is a defintion, not a rest... | Relationship between Linear Projection and OLS Regression | We start with the population model:
$$\begin{aligned}y &=E(y|{\bf x)}+e \\ E(e|{\bf x}) &=0 \end{aligned}$$
where $m({\bf x})=E(y|x)$ is the conditional expectation function (CEF) and e is the CEF er | Relationship between Linear Projection and OLS Regression
We start with the population model:
$$\begin{aligned}y &=E(y|{\bf x)}+e \\ E(e|{\bf x}) &=0 \end{aligned}$$
where $m({\bf x})=E(y|x)$ is the conditional expectation function (CEF) and e is the CEF error. It is important to note that the equations above are a de... | Relationship between Linear Projection and OLS Regression
We start with the population model:
$$\begin{aligned}y &=E(y|{\bf x)}+e \\ E(e|{\bf x}) &=0 \end{aligned}$$
where $m({\bf x})=E(y|x)$ is the conditional expectation function (CEF) and e is the CEF er |
42,369 | Conditional expectation for non-gaussian variables | Without making additional assumptions, there is little one can say.
The idea behind the examples given here is that in the regression of $A$ against $B$, we may take a tiny bit of the range of $B$ (for which there is low probability $q$) and drastically alter the conditional distribution of $A$ in that range without ch... | Conditional expectation for non-gaussian variables | Without making additional assumptions, there is little one can say.
The idea behind the examples given here is that in the regression of $A$ against $B$, we may take a tiny bit of the range of $B$ (fo | Conditional expectation for non-gaussian variables
Without making additional assumptions, there is little one can say.
The idea behind the examples given here is that in the regression of $A$ against $B$, we may take a tiny bit of the range of $B$ (for which there is low probability $q$) and drastically alter the condi... | Conditional expectation for non-gaussian variables
Without making additional assumptions, there is little one can say.
The idea behind the examples given here is that in the regression of $A$ against $B$, we may take a tiny bit of the range of $B$ (fo |
42,370 | Reporting of Neural Network Accuracy for Academic Publications | Before I answer your question let me say, in general it is considered good practice to have a Validation set that is completely distinct from your Test set. You may or may not be aware of this fact, but you seem to gloss over it in your question, so I wanted to make that explicitly clear.
The answer to your question w... | Reporting of Neural Network Accuracy for Academic Publications | Before I answer your question let me say, in general it is considered good practice to have a Validation set that is completely distinct from your Test set. You may or may not be aware of this fact, b | Reporting of Neural Network Accuracy for Academic Publications
Before I answer your question let me say, in general it is considered good practice to have a Validation set that is completely distinct from your Test set. You may or may not be aware of this fact, but you seem to gloss over it in your question, so I wante... | Reporting of Neural Network Accuracy for Academic Publications
Before I answer your question let me say, in general it is considered good practice to have a Validation set that is completely distinct from your Test set. You may or may not be aware of this fact, b |
42,371 | Sampling from Truncated Distribution in STAN | Given that xtrue[i]'s are constrained, Stan requires that these constraints are included in the variable declaration. To my knowledge, these constraints must be scalar quantities.
Below, I worked around this requirement by considering auxillary parameters, xraw[i], which have a truncated normal distribution.
m <- "
dat... | Sampling from Truncated Distribution in STAN | Given that xtrue[i]'s are constrained, Stan requires that these constraints are included in the variable declaration. To my knowledge, these constraints must be scalar quantities.
Below, I worked arou | Sampling from Truncated Distribution in STAN
Given that xtrue[i]'s are constrained, Stan requires that these constraints are included in the variable declaration. To my knowledge, these constraints must be scalar quantities.
Below, I worked around this requirement by considering auxillary parameters, xraw[i], which hav... | Sampling from Truncated Distribution in STAN
Given that xtrue[i]'s are constrained, Stan requires that these constraints are included in the variable declaration. To my knowledge, these constraints must be scalar quantities.
Below, I worked arou |
42,372 | Add New Object Class in Deep Learning Network | You should, at least, re-train the classification layer. When you add another output, during learning the new class, the other class activation must shrink either explicitly (sigmoid) or implicitly (softmax). However, it may be better to learn, at least, last feature layer as there would be some useful features to reco... | Add New Object Class in Deep Learning Network | You should, at least, re-train the classification layer. When you add another output, during learning the new class, the other class activation must shrink either explicitly (sigmoid) or implicitly (s | Add New Object Class in Deep Learning Network
You should, at least, re-train the classification layer. When you add another output, during learning the new class, the other class activation must shrink either explicitly (sigmoid) or implicitly (softmax). However, it may be better to learn, at least, last feature layer ... | Add New Object Class in Deep Learning Network
You should, at least, re-train the classification layer. When you add another output, during learning the new class, the other class activation must shrink either explicitly (sigmoid) or implicitly (s |
42,373 | Add New Object Class in Deep Learning Network | A few years after the question was asked, there are several attempt to solve this problem.
My best guess would be to:
add a class in the last layer
train the class corresponding to pedestrian with the new data
try not to move the way the network was predicting the other class by using distillation. This may need som... | Add New Object Class in Deep Learning Network | A few years after the question was asked, there are several attempt to solve this problem.
My best guess would be to:
add a class in the last layer
train the class corresponding to pedestrian with | Add New Object Class in Deep Learning Network
A few years after the question was asked, there are several attempt to solve this problem.
My best guess would be to:
add a class in the last layer
train the class corresponding to pedestrian with the new data
try not to move the way the network was predicting the other ... | Add New Object Class in Deep Learning Network
A few years after the question was asked, there are several attempt to solve this problem.
My best guess would be to:
add a class in the last layer
train the class corresponding to pedestrian with |
42,374 | Add New Object Class in Deep Learning Network | In order to add a class you will almost certainly need a differently structured network (ie. +1 output). You may also require more hidden nodes or inputs depending on your problem. Of course, as you mentioned you could simply re-train the parameters based on all the new data, however, you will lose all the benefit of t... | Add New Object Class in Deep Learning Network | In order to add a class you will almost certainly need a differently structured network (ie. +1 output). You may also require more hidden nodes or inputs depending on your problem. Of course, as you m | Add New Object Class in Deep Learning Network
In order to add a class you will almost certainly need a differently structured network (ie. +1 output). You may also require more hidden nodes or inputs depending on your problem. Of course, as you mentioned you could simply re-train the parameters based on all the new dat... | Add New Object Class in Deep Learning Network
In order to add a class you will almost certainly need a differently structured network (ie. +1 output). You may also require more hidden nodes or inputs depending on your problem. Of course, as you m |
42,375 | Add New Object Class in Deep Learning Network | I don't know if it would work, but one approach would be to add a new neuron in the output softmax layer and train with gradient descent but updating only the weights going to that new class.
It is probably a very sub-optimal method but it could be worth trying. | Add New Object Class in Deep Learning Network | I don't know if it would work, but one approach would be to add a new neuron in the output softmax layer and train with gradient descent but updating only the weights going to that new class.
It is pr | Add New Object Class in Deep Learning Network
I don't know if it would work, but one approach would be to add a new neuron in the output softmax layer and train with gradient descent but updating only the weights going to that new class.
It is probably a very sub-optimal method but it could be worth trying. | Add New Object Class in Deep Learning Network
I don't know if it would work, but one approach would be to add a new neuron in the output softmax layer and train with gradient descent but updating only the weights going to that new class.
It is pr |
42,376 | When does pam (partition around medoids) fails to find the optimal solution? (counter example?!) | Try to build one!
We need a cluster assignment that is stable, but not optimal.
So the medoids bust be the medoids of their clusters, and every object is assigned to its closest medoid. But there is a solution that is a lot better.
Lets start simple. One dimensional, three points per cluster, stars indicate the cluster... | When does pam (partition around medoids) fails to find the optimal solution? (counter example?!) | Try to build one!
We need a cluster assignment that is stable, but not optimal.
So the medoids bust be the medoids of their clusters, and every object is assigned to its closest medoid. But there is a | When does pam (partition around medoids) fails to find the optimal solution? (counter example?!)
Try to build one!
We need a cluster assignment that is stable, but not optimal.
So the medoids bust be the medoids of their clusters, and every object is assigned to its closest medoid. But there is a solution that is a lot... | When does pam (partition around medoids) fails to find the optimal solution? (counter example?!)
Try to build one!
We need a cluster assignment that is stable, but not optimal.
So the medoids bust be the medoids of their clusters, and every object is assigned to its closest medoid. But there is a |
42,377 | When does pam (partition around medoids) fails to find the optimal solution? (counter example?!) | Clustering is an NP-complete problem - see proof fo the one definition of clustering and another definition of clustering.
The question of whether P=NP wasn't answered yet but is seems as if the complexity classes are different.
If it is so, then no polynomial time algorithm, like the pam algorithm can solve the proble... | When does pam (partition around medoids) fails to find the optimal solution? (counter example?!) | Clustering is an NP-complete problem - see proof fo the one definition of clustering and another definition of clustering.
The question of whether P=NP wasn't answered yet but is seems as if the compl | When does pam (partition around medoids) fails to find the optimal solution? (counter example?!)
Clustering is an NP-complete problem - see proof fo the one definition of clustering and another definition of clustering.
The question of whether P=NP wasn't answered yet but is seems as if the complexity classes are diffe... | When does pam (partition around medoids) fails to find the optimal solution? (counter example?!)
Clustering is an NP-complete problem - see proof fo the one definition of clustering and another definition of clustering.
The question of whether P=NP wasn't answered yet but is seems as if the compl |
42,378 | Interpretation of level, trend and seasonal indices in Holt-Winters exponential smoothing | As you know, you will first initialize all three indices, then update them over the historical period, then extrapolate them out into the future to derive the forecast from their extrapolated values.
At any given time, the level index gives an estimate of the local mean, or "level" of the data-generating process (DGP),... | Interpretation of level, trend and seasonal indices in Holt-Winters exponential smoothing | As you know, you will first initialize all three indices, then update them over the historical period, then extrapolate them out into the future to derive the forecast from their extrapolated values.
| Interpretation of level, trend and seasonal indices in Holt-Winters exponential smoothing
As you know, you will first initialize all three indices, then update them over the historical period, then extrapolate them out into the future to derive the forecast from their extrapolated values.
At any given time, the level i... | Interpretation of level, trend and seasonal indices in Holt-Winters exponential smoothing
As you know, you will first initialize all three indices, then update them over the historical period, then extrapolate them out into the future to derive the forecast from their extrapolated values.
|
42,379 | Resources for understanding Gaussian processes | Chapter 6.4 of Bishop's PRML book explains Gaussian Processes for regression and classification. In addition, Murphy's ML book discusses GP latent variable models and connections with other methods. The web-site (http://www.gaussianprocess.org/) accompanying the GP book has additional implementation resources. | Resources for understanding Gaussian processes | Chapter 6.4 of Bishop's PRML book explains Gaussian Processes for regression and classification. In addition, Murphy's ML book discusses GP latent variable models and connections with other methods. T | Resources for understanding Gaussian processes
Chapter 6.4 of Bishop's PRML book explains Gaussian Processes for regression and classification. In addition, Murphy's ML book discusses GP latent variable models and connections with other methods. The web-site (http://www.gaussianprocess.org/) accompanying the GP book ha... | Resources for understanding Gaussian processes
Chapter 6.4 of Bishop's PRML book explains Gaussian Processes for regression and classification. In addition, Murphy's ML book discusses GP latent variable models and connections with other methods. T |
42,380 | Resources for understanding Gaussian processes | I highly recommend the Distill.pub article on visual exploration of Gaussian processes that provides a lot of intuitive understanding. You can also watch the video recordings of past Gaussian process summer schools. | Resources for understanding Gaussian processes | I highly recommend the Distill.pub article on visual exploration of Gaussian processes that provides a lot of intuitive understanding. You can also watch the video recordings of past Gaussian process | Resources for understanding Gaussian processes
I highly recommend the Distill.pub article on visual exploration of Gaussian processes that provides a lot of intuitive understanding. You can also watch the video recordings of past Gaussian process summer schools. | Resources for understanding Gaussian processes
I highly recommend the Distill.pub article on visual exploration of Gaussian processes that provides a lot of intuitive understanding. You can also watch the video recordings of past Gaussian process |
42,381 | Resources for understanding Gaussian processes | I read the scikit-learn documentation on Gaussian processes in order to learn the basics. | Resources for understanding Gaussian processes | I read the scikit-learn documentation on Gaussian processes in order to learn the basics. | Resources for understanding Gaussian processes
I read the scikit-learn documentation on Gaussian processes in order to learn the basics. | Resources for understanding Gaussian processes
I read the scikit-learn documentation on Gaussian processes in order to learn the basics. |
42,382 | When to use RBF networks instead of multilayer perceptron? | You may use RBF networks in case you do not necessarily need to have multiple hidden layers in your model and more importantly, you want your model to be robust to adversarial noise/examples. The advantage of RBF networks is they bring much more robustness to your prediction, but as mentioned earlier they are more limi... | When to use RBF networks instead of multilayer perceptron? | You may use RBF networks in case you do not necessarily need to have multiple hidden layers in your model and more importantly, you want your model to be robust to adversarial noise/examples. The adva | When to use RBF networks instead of multilayer perceptron?
You may use RBF networks in case you do not necessarily need to have multiple hidden layers in your model and more importantly, you want your model to be robust to adversarial noise/examples. The advantage of RBF networks is they bring much more robustness to y... | When to use RBF networks instead of multilayer perceptron?
You may use RBF networks in case you do not necessarily need to have multiple hidden layers in your model and more importantly, you want your model to be robust to adversarial noise/examples. The adva |
42,383 | Bayes theorem in odds form - incorrect in Tetlock's 'Superforecasting' book? | So this doesn't remain unanswered (except in comments) let's derive the odds ratio form from scratch:
We know
$P(A|B)=\frac{P(B|A)\cdot P(A)}{P(B)}$, so:
$$\frac{P(A|B)}{P(\bar{A}|B)}=\frac{\frac{P(B|A)\cdot P(A)}{P(B)}}{\frac{P(B|\bar{A})\cdot P(\bar{A})}{P(B)}}$$
$$=\frac{P(B|A)\cdot P(A)}{{P(B|\bar{A})\cdot P(\bar{... | Bayes theorem in odds form - incorrect in Tetlock's 'Superforecasting' book? | So this doesn't remain unanswered (except in comments) let's derive the odds ratio form from scratch:
We know
$P(A|B)=\frac{P(B|A)\cdot P(A)}{P(B)}$, so:
$$\frac{P(A|B)}{P(\bar{A}|B)}=\frac{\frac{P(B | Bayes theorem in odds form - incorrect in Tetlock's 'Superforecasting' book?
So this doesn't remain unanswered (except in comments) let's derive the odds ratio form from scratch:
We know
$P(A|B)=\frac{P(B|A)\cdot P(A)}{P(B)}$, so:
$$\frac{P(A|B)}{P(\bar{A}|B)}=\frac{\frac{P(B|A)\cdot P(A)}{P(B)}}{\frac{P(B|\bar{A})\cd... | Bayes theorem in odds form - incorrect in Tetlock's 'Superforecasting' book?
So this doesn't remain unanswered (except in comments) let's derive the odds ratio form from scratch:
We know
$P(A|B)=\frac{P(B|A)\cdot P(A)}{P(B)}$, so:
$$\frac{P(A|B)}{P(\bar{A}|B)}=\frac{\frac{P(B |
42,384 | What are some of the potential consequences of adding junk controls in your regression? | We can write the formula for the standard error of a regression coefficient $\hat \beta_j$ as
$$\sqrt{\frac{\sum_{i=1}^n \hat u^2}{(n-k-1)\sum_{i=1}^n(x_{ij}-\bar x_j)^2(1-R^2_j)}}$$
where $\hat u^2$ are the regression residuals, $n$ is the numbers of observations, $k$ is the number of regressors, and $R^2_j$ is the $R... | What are some of the potential consequences of adding junk controls in your regression? | We can write the formula for the standard error of a regression coefficient $\hat \beta_j$ as
$$\sqrt{\frac{\sum_{i=1}^n \hat u^2}{(n-k-1)\sum_{i=1}^n(x_{ij}-\bar x_j)^2(1-R^2_j)}}$$
where $\hat u^2$ | What are some of the potential consequences of adding junk controls in your regression?
We can write the formula for the standard error of a regression coefficient $\hat \beta_j$ as
$$\sqrt{\frac{\sum_{i=1}^n \hat u^2}{(n-k-1)\sum_{i=1}^n(x_{ij}-\bar x_j)^2(1-R^2_j)}}$$
where $\hat u^2$ are the regression residuals, $n... | What are some of the potential consequences of adding junk controls in your regression?
We can write the formula for the standard error of a regression coefficient $\hat \beta_j$ as
$$\sqrt{\frac{\sum_{i=1}^n \hat u^2}{(n-k-1)\sum_{i=1}^n(x_{ij}-\bar x_j)^2(1-R^2_j)}}$$
where $\hat u^2$ |
42,385 | Sequential Update of Bayesian | Indeed - you can update sequentially or in a batch fashion so long as you assume exchangeability. It's analogous to the iid assumption typically made in frequentist models.
In this case, $D_{a}$ and $D_{b}$ exchangeable implies that $P(D_{a}, D_{b} \, | \, \theta) = P(D_{a} \, | \, \theta) P(D_{b} \, | \, \theta)$ f... | Sequential Update of Bayesian | Indeed - you can update sequentially or in a batch fashion so long as you assume exchangeability. It's analogous to the iid assumption typically made in frequentist models.
In this case, $D_{a}$ an | Sequential Update of Bayesian
Indeed - you can update sequentially or in a batch fashion so long as you assume exchangeability. It's analogous to the iid assumption typically made in frequentist models.
In this case, $D_{a}$ and $D_{b}$ exchangeable implies that $P(D_{a}, D_{b} \, | \, \theta) = P(D_{a} \, | \, \the... | Sequential Update of Bayesian
Indeed - you can update sequentially or in a batch fashion so long as you assume exchangeability. It's analogous to the iid assumption typically made in frequentist models.
In this case, $D_{a}$ an |
42,386 | Sequential Update of Bayesian | Actually, the general formula of sequential Bayesian updating is:
$$
P(\theta \mid D_{a}, D_{b}) \propto P(D_b \mid \theta, D_a) P(\theta \mid D_a).
\,\,\,(*)
$$
However, for most machine learning models,
$D_a$ and $D_b$ are conditionally independent given $\theta$, i.e.,
$$
P(D_a \mid \theta) P(D_b \mid \theta) = ... | Sequential Update of Bayesian | Actually, the general formula of sequential Bayesian updating is:
$$
P(\theta \mid D_{a}, D_{b}) \propto P(D_b \mid \theta, D_a) P(\theta \mid D_a).
\,\,\,(*)
$$
However, for most machine learning mo | Sequential Update of Bayesian
Actually, the general formula of sequential Bayesian updating is:
$$
P(\theta \mid D_{a}, D_{b}) \propto P(D_b \mid \theta, D_a) P(\theta \mid D_a).
\,\,\,(*)
$$
However, for most machine learning models,
$D_a$ and $D_b$ are conditionally independent given $\theta$, i.e.,
$$
P(D_a \mid... | Sequential Update of Bayesian
Actually, the general formula of sequential Bayesian updating is:
$$
P(\theta \mid D_{a}, D_{b}) \propto P(D_b \mid \theta, D_a) P(\theta \mid D_a).
\,\,\,(*)
$$
However, for most machine learning mo |
42,387 | What does a copula density explain about dependence of random variables? | Well, the copula density is a density and can be interpreted as any other density. Specifically, with the density you have shown us, clearly the conditional distribution of one variable depends on the other, so there is dependence, not independence. Further, the density is higher (highest) for (0,0) and (1,1), and low... | What does a copula density explain about dependence of random variables? | Well, the copula density is a density and can be interpreted as any other density. Specifically, with the density you have shown us, clearly the conditional distribution of one variable depends on th | What does a copula density explain about dependence of random variables?
Well, the copula density is a density and can be interpreted as any other density. Specifically, with the density you have shown us, clearly the conditional distribution of one variable depends on the other, so there is dependence, not independen... | What does a copula density explain about dependence of random variables?
Well, the copula density is a density and can be interpreted as any other density. Specifically, with the density you have shown us, clearly the conditional distribution of one variable depends on th |
42,388 | Adadelta idea 1 vs RMSprop | Referring the links pointed out by you, RMSprop is focussed on updating the learning rate $\eta$ for each iteration using accumulation of square of gradients:
$r_{t}=\rho r_{t-1} + (1-\rho)g_{t}^{2}$ [where g is the gradient] and plugging to find the effective learning rate at step t using: $\eta_{t}=\frac{\eta}{\sqrt[... | Adadelta idea 1 vs RMSprop | Referring the links pointed out by you, RMSprop is focussed on updating the learning rate $\eta$ for each iteration using accumulation of square of gradients:
$r_{t}=\rho r_{t-1} + (1-\rho)g_{t}^{2}$ | Adadelta idea 1 vs RMSprop
Referring the links pointed out by you, RMSprop is focussed on updating the learning rate $\eta$ for each iteration using accumulation of square of gradients:
$r_{t}=\rho r_{t-1} + (1-\rho)g_{t}^{2}$ [where g is the gradient] and plugging to find the effective learning rate at step t using: $... | Adadelta idea 1 vs RMSprop
Referring the links pointed out by you, RMSprop is focussed on updating the learning rate $\eta$ for each iteration using accumulation of square of gradients:
$r_{t}=\rho r_{t-1} + (1-\rho)g_{t}^{2}$ |
42,389 | Adadelta idea 1 vs RMSprop | Yes, you are correct.
Like you, I also arrived at the same conclusion by examining Idea 1 (section 3.1) in the Adadelta paper and the lecture.
Anyway, here is some more evidence:
Sebastian Ruder wrote in his popular blog post An overview of gradient descent optimization algorithms:
RMSprop and Adadelta have both been... | Adadelta idea 1 vs RMSprop | Yes, you are correct.
Like you, I also arrived at the same conclusion by examining Idea 1 (section 3.1) in the Adadelta paper and the lecture.
Anyway, here is some more evidence:
Sebastian Ruder wrot | Adadelta idea 1 vs RMSprop
Yes, you are correct.
Like you, I also arrived at the same conclusion by examining Idea 1 (section 3.1) in the Adadelta paper and the lecture.
Anyway, here is some more evidence:
Sebastian Ruder wrote in his popular blog post An overview of gradient descent optimization algorithms:
RMSprop ... | Adadelta idea 1 vs RMSprop
Yes, you are correct.
Like you, I also arrived at the same conclusion by examining Idea 1 (section 3.1) in the Adadelta paper and the lecture.
Anyway, here is some more evidence:
Sebastian Ruder wrot |
42,390 | Model construction: when to shuffle data and when to sort it? | Pre-treatment
If your data comes from different sources (different sensors, stores...) and you want to split it into train/validation sets, yes it is generally safer to shuffle it, as the partitions you will create will come from different sources.
Say some patients come from various hospitals and you want to predict ... | Model construction: when to shuffle data and when to sort it? | Pre-treatment
If your data comes from different sources (different sensors, stores...) and you want to split it into train/validation sets, yes it is generally safer to shuffle it, as the partitions y | Model construction: when to shuffle data and when to sort it?
Pre-treatment
If your data comes from different sources (different sensors, stores...) and you want to split it into train/validation sets, yes it is generally safer to shuffle it, as the partitions you will create will come from different sources.
Say some... | Model construction: when to shuffle data and when to sort it?
Pre-treatment
If your data comes from different sources (different sensors, stores...) and you want to split it into train/validation sets, yes it is generally safer to shuffle it, as the partitions y |
42,391 | Multicollinearity in WLS regression | It's a great question, because it concerns an important and fundamental issue about multiple regression. The answer is that weighting can change the VIF (a measure of collinearity among the regressors) by arbitrary amounts up or down (with a lower limit of $1$).
To see why that is, consider a model with two regressors... | Multicollinearity in WLS regression | It's a great question, because it concerns an important and fundamental issue about multiple regression. The answer is that weighting can change the VIF (a measure of collinearity among the regressor | Multicollinearity in WLS regression
It's a great question, because it concerns an important and fundamental issue about multiple regression. The answer is that weighting can change the VIF (a measure of collinearity among the regressors) by arbitrary amounts up or down (with a lower limit of $1$).
To see why that is, ... | Multicollinearity in WLS regression
It's a great question, because it concerns an important and fundamental issue about multiple regression. The answer is that weighting can change the VIF (a measure of collinearity among the regressor |
42,392 | Truncated Back-propagation through time for RNNs | I had wondered the same thing, and it looks like this has been thoroughly researched in this nice blog post:
http://r2rt.com/styles-of-truncated-backpropagation.html (mirror)
The author found that the disjoint approach, which is what TensorFlow uses, works reasonably well. Using a sliding window every time step is diff... | Truncated Back-propagation through time for RNNs | I had wondered the same thing, and it looks like this has been thoroughly researched in this nice blog post:
http://r2rt.com/styles-of-truncated-backpropagation.html (mirror)
The author found that the | Truncated Back-propagation through time for RNNs
I had wondered the same thing, and it looks like this has been thoroughly researched in this nice blog post:
http://r2rt.com/styles-of-truncated-backpropagation.html (mirror)
The author found that the disjoint approach, which is what TensorFlow uses, works reasonably wel... | Truncated Back-propagation through time for RNNs
I had wondered the same thing, and it looks like this has been thoroughly researched in this nice blog post:
http://r2rt.com/styles-of-truncated-backpropagation.html (mirror)
The author found that the |
42,393 | Discretizing a Continuous Input for an Artificial Neural Network | You're touching on problem formulation, where the designer defines input variables and their characteristics. Looking at the structure you show, if you were trying to predict whether a liquid is frozen or solid, such a structure would be useful. If you were trying to predict a future value of the variable, then it wo... | Discretizing a Continuous Input for an Artificial Neural Network | You're touching on problem formulation, where the designer defines input variables and their characteristics. Looking at the structure you show, if you were trying to predict whether a liquid is froz | Discretizing a Continuous Input for an Artificial Neural Network
You're touching on problem formulation, where the designer defines input variables and their characteristics. Looking at the structure you show, if you were trying to predict whether a liquid is frozen or solid, such a structure would be useful. If you ... | Discretizing a Continuous Input for an Artificial Neural Network
You're touching on problem formulation, where the designer defines input variables and their characteristics. Looking at the structure you show, if you were trying to predict whether a liquid is froz |
42,394 | What is the difference between Apriori and Eclat algorithms? | Here is a good description:
http://www.slideshare.net/wanaezwani/apriori-and-eclat-algorithm-in-association-rule-mining
In particular, apriori is probably the first association rule mining and computationally complex. This leads to the introduction of further fast algorithms. | What is the difference between Apriori and Eclat algorithms? | Here is a good description:
http://www.slideshare.net/wanaezwani/apriori-and-eclat-algorithm-in-association-rule-mining
In particular, apriori is probably the first association rule mining and computa | What is the difference between Apriori and Eclat algorithms?
Here is a good description:
http://www.slideshare.net/wanaezwani/apriori-and-eclat-algorithm-in-association-rule-mining
In particular, apriori is probably the first association rule mining and computationally complex. This leads to the introduction of further... | What is the difference between Apriori and Eclat algorithms?
Here is a good description:
http://www.slideshare.net/wanaezwani/apriori-and-eclat-algorithm-in-association-rule-mining
In particular, apriori is probably the first association rule mining and computa |
42,395 | What is the difference between Apriori and Eclat algorithms? | Apriori is useable with large datasets and Eclat is better suited to small and medium datasets.
Apriori scans the original (real) dataset, whereas Eclat scan the currently generated dataset.
Apriori is slower than Eclat. | What is the difference between Apriori and Eclat algorithms? | Apriori is useable with large datasets and Eclat is better suited to small and medium datasets.
Apriori scans the original (real) dataset, whereas Eclat scan the currently generated dataset.
Apriori i | What is the difference between Apriori and Eclat algorithms?
Apriori is useable with large datasets and Eclat is better suited to small and medium datasets.
Apriori scans the original (real) dataset, whereas Eclat scan the currently generated dataset.
Apriori is slower than Eclat. | What is the difference between Apriori and Eclat algorithms?
Apriori is useable with large datasets and Eclat is better suited to small and medium datasets.
Apriori scans the original (real) dataset, whereas Eclat scan the currently generated dataset.
Apriori i |
42,396 | What is the difference between Apriori and Eclat algorithms? | Apriori algorithm is a classical algorithm used to mining the frequent item sets in a given dataset.
Coming to Eclat algorithm also mining the frequent itemsets but in vertical manner and it follows the depth first search of a graph.
As per the speed,Eclat is fast than the Apriori algorithm.
Apriori works on larger da... | What is the difference between Apriori and Eclat algorithms? | Apriori algorithm is a classical algorithm used to mining the frequent item sets in a given dataset.
Coming to Eclat algorithm also mining the frequent itemsets but in vertical manner and it follows | What is the difference between Apriori and Eclat algorithms?
Apriori algorithm is a classical algorithm used to mining the frequent item sets in a given dataset.
Coming to Eclat algorithm also mining the frequent itemsets but in vertical manner and it follows the depth first search of a graph.
As per the speed,Eclat i... | What is the difference between Apriori and Eclat algorithms?
Apriori algorithm is a classical algorithm used to mining the frequent item sets in a given dataset.
Coming to Eclat algorithm also mining the frequent itemsets but in vertical manner and it follows |
42,397 | What is the difference between Apriori and Eclat algorithms? | Look this article:
Comparing Dataset Characteristics that Favor the
Apriori, Eclat or FP-Growth Frequent Itemset
Mining Algorithms
Apriori is an easily understandable frequent itemset mining
algorithm. Because of this, Apriori is a popular starting
point for frequent itemset study. However, Apriori has serious
scalabi... | What is the difference between Apriori and Eclat algorithms? | Look this article:
Comparing Dataset Characteristics that Favor the
Apriori, Eclat or FP-Growth Frequent Itemset
Mining Algorithms
Apriori is an easily understandable frequent itemset mining
algorith | What is the difference between Apriori and Eclat algorithms?
Look this article:
Comparing Dataset Characteristics that Favor the
Apriori, Eclat or FP-Growth Frequent Itemset
Mining Algorithms
Apriori is an easily understandable frequent itemset mining
algorithm. Because of this, Apriori is a popular starting
point for... | What is the difference between Apriori and Eclat algorithms?
Look this article:
Comparing Dataset Characteristics that Favor the
Apriori, Eclat or FP-Growth Frequent Itemset
Mining Algorithms
Apriori is an easily understandable frequent itemset mining
algorith |
42,398 | which book can help me for self-learning VARIMA model? | VARIMA model are discussed in http://www.amazon.co.uk/Time-Series-Analysis-Univariate-Multivariate/dp/0201159112 . It has exanples using MTS software. VAR models are easier to compute which is why they are more popular but who said statistical analysis would be easy. | which book can help me for self-learning VARIMA model? | VARIMA model are discussed in http://www.amazon.co.uk/Time-Series-Analysis-Univariate-Multivariate/dp/0201159112 . It has exanples using MTS software. VAR models are easier to compute which is why the | which book can help me for self-learning VARIMA model?
VARIMA model are discussed in http://www.amazon.co.uk/Time-Series-Analysis-Univariate-Multivariate/dp/0201159112 . It has exanples using MTS software. VAR models are easier to compute which is why they are more popular but who said statistical analysis would be eas... | which book can help me for self-learning VARIMA model?
VARIMA model are discussed in http://www.amazon.co.uk/Time-Series-Analysis-Univariate-Multivariate/dp/0201159112 . It has exanples using MTS software. VAR models are easier to compute which is why the |
42,399 | which book can help me for self-learning VARIMA model? | I highly recommend Luktepohl's "New Introduction to Multiple Time Series Analysis" or Tsay's "Multivariate Time Series," both of which are specifically dedicated to multivarate time series analysis. Both texts contain much more if you are so inclined to read beyond VARIMA. Tsay even has his own multivariate time series... | which book can help me for self-learning VARIMA model? | I highly recommend Luktepohl's "New Introduction to Multiple Time Series Analysis" or Tsay's "Multivariate Time Series," both of which are specifically dedicated to multivarate time series analysis. B | which book can help me for self-learning VARIMA model?
I highly recommend Luktepohl's "New Introduction to Multiple Time Series Analysis" or Tsay's "Multivariate Time Series," both of which are specifically dedicated to multivarate time series analysis. Both texts contain much more if you are so inclined to read beyond... | which book can help me for self-learning VARIMA model?
I highly recommend Luktepohl's "New Introduction to Multiple Time Series Analysis" or Tsay's "Multivariate Time Series," both of which are specifically dedicated to multivarate time series analysis. B |
42,400 | Statistical significance test for averages of correlation coefficients | Short answer: Yes, transform each correlation ($AB_i$ and $CD_i$) using the Fisher $r-to-z'$ transform:
$f(r)=\frac12 ln \frac{1+r}{1-r}$.
Then, perform an independent samples t-test to test the null hypothesis of $\mu_{f(AB)}=\mu_{f(CD)}$.
Rationale: You're right that approximate normality is important here. With ... | Statistical significance test for averages of correlation coefficients | Short answer: Yes, transform each correlation ($AB_i$ and $CD_i$) using the Fisher $r-to-z'$ transform:
$f(r)=\frac12 ln \frac{1+r}{1-r}$.
Then, perform an independent samples t-test to test the nul | Statistical significance test for averages of correlation coefficients
Short answer: Yes, transform each correlation ($AB_i$ and $CD_i$) using the Fisher $r-to-z'$ transform:
$f(r)=\frac12 ln \frac{1+r}{1-r}$.
Then, perform an independent samples t-test to test the null hypothesis of $\mu_{f(AB)}=\mu_{f(CD)}$.
Ratio... | Statistical significance test for averages of correlation coefficients
Short answer: Yes, transform each correlation ($AB_i$ and $CD_i$) using the Fisher $r-to-z'$ transform:
$f(r)=\frac12 ln \frac{1+r}{1-r}$.
Then, perform an independent samples t-test to test the nul |
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