| --- |
| tags: |
| - lan |
| - ssm |
| - hssm |
| - angle |
| - collapsing-bound |
| library_name: onnx |
| license: bsd-2-clause |
| --- |
| |
| # angle_extended (LAN) |
| |
| Likelihood approximation network for the angle_extended sequential sampling model: the standard angle model -- constant drift v with a linearly collapsing decision bound whose collapse angle is theta -- with the drift bounds widened from (-3, 3) to (-6, 6) for designs that produce strong evidence. Parameters are v, a, z, t, theta, in that order, and the network was trained on the full ssm-simulators bounds for the model. |
| Recovery verdict: PASS. Judged alone on a 240-fit sweep (12 design cells x 20 paired datasets, 81 family-corrected tests) this network records zero coverage failures, zero bias failures, and zero cells the design could not identify, with 84% of parameter records converged (r-hat <= 1.01, bulk ESS >= 400). The extended drift range is exercised, not merely declared: density checks and recovery truths reach |v| near 4.5 and beyond. The sweep contains no analytical or blackbox reference arm -- none exists for a collapsing- bound model -- so, as with every LAN in this family, coverage is judged against nominal rather than against an exact-likelihood ceiling. |
| Density: the worst Hellinger distance to a 20k-trial simulation is 0.054, which is BELOW the Monte Carlo sampling floor at that sample size (ratio 0.92 against a gate bar of 3.0) -- the network is closer to the simulator than the simulator is to itself at 20k trials. Total probability mass stays within 0.4% of 1.0 on every draw. |
| A minority of truth draws produce genuinely hard posteriors -- long sampler trajectories, occasional non-convergence; wall time and convergence correlate at -0.50 across the sweep. This is posterior geometry at specific truths, not a defect localised to the network, and no design cell fell below the judgement threshold because of it. |
|
|
| ## Architecture |
|
|
| - **Network Type:** lan |
| - **Layer Sizes:** [128, 128, 128, 128, 1] |
| - **Activations:** ['tanh', 'tanh', 'tanh', 'tanh', 'linear'] |
|
|
| ## Training |
|
|
| - **Epochs:** 20 |
| - **Optimizer:** adam |
| - **Learning Rate:** 0.001 |
| - **Loss:** huber |
|
|
| ## Usage with HSSM |
|
|
| ```python |
| import hssm |
| |
| # p_outlier=0.0 is deliberate. HSSM attaches a 5% lapse by default, but the |
| # training data contains no lapses, so the default misspecifies every fit -- |
| # the bias stays fixed while the posterior sd shrinks as 1/sqrt(n), so |
| # coverage gets WORSE as the dataset grows. Set a lapse only if your data |
| # actually has one. |
| model = hssm.HSSM(data=data, model="angle_extended", p_outlier=0.0) |
| |
| # On an HSSM release that predates the built-in config, pass the network |
| # and the box explicitly. Keep the parameter order exactly as written: it |
| # is the ONNX input column order, and a wrong order does not raise -- it |
| # silently evaluates the wrong likelihood. |
| model = hssm.HSSM( |
| data=data, |
| model="angle_extended", |
| loglik="angle_extended.onnx", |
| loglik_kind="approx_differentiable", |
| p_outlier=0.0, |
| model_config={ |
| "list_params": ["v", "a", "z", "t", "theta"], |
| "backend": "jax", |
| "bounds": { |
| "v": (-6.0, 6.0), "a": (0.3, 3.0), "z": (0.1, 0.9), |
| "t": (0.001, 2.0), "theta": (-0.1, 1.3), |
| }, |
| }, |
| ) |
| ``` |
|
|
| ## Citation |
|
|
| If you use this model, please cite: |
|
|
| - [LANfactory](https://github.com/lnccbrown/LANfactory) |
| - [HSSM](https://github.com/lnccbrown/HSSM) |
|
|
