Download lean/OAI/Probability/SKBarriers/Scalar/PathExponentialAverage.lean from roseking/openAI-math-dataset: direct link, hf CLI and curl.
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https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/OAI/Probability/SKBarriers/Scalar/PathExponentialAverage.lean
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hf download hf://datasets/roseking/openAI-math-dataset/lean/OAI/Probability/SKBarriers/Scalar/PathExponentialAverage.lean
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curl -L -o PathExponentialAverage.lean https://huggingface.co/datasets/roseking/openAI-math-dataset/resolve/main/lean/OAI/Probability/SKBarriers/Scalar/PathExponentialAverage.lean
2.46 kB
| import Mathlib | |
| import OAI.Probability.SKBarriers.Hierarchy.HierarchyPathLaw | |
| import OAI.Probability.SKBarriers.Gaussian.ExponentialAverage | |
| namespace OAI | |
| section | |
| section | |
| noncomputable section | |
| open scoped BigOperators | |
| open MeasureTheory ProbabilityTheory Filter | |
| namespace SK.Analytic | |
| attribute [local instance 2000] parameterNormedGroup parameterNormedSpace | |
| theorem hierarchyPath_integral_succ_exp (n : ℕ) (m : Fin (n+1) → ℝ) | |
| (f : ParameterSpace (n+1) → ℝ) (hf : BoundedDerivs f) (x : ℝ) | |
| (g : ParameterSpace (n+1) → ℝ) (hg : Continuous g) (hb : HasExpGrowth g) : | |
| (∫ z, g z ∂hierarchyPathLaw (n+1) m f x) = | |
| ∫ z, gaussianAverage (m (Fin.last n)) f g z | |
| ∂hierarchyPathLaw n (fun i => m i.castSucc) (gaussianStep (m (Fin.last n)) f) x := by | |
| rw [hierarchyPathLaw_integral (n+1) m f hf, | |
| hierarchyPathLaw_integral n _ _ (hf.gaussianStep _),fiberGaussian, | |
| integral_prod _ (hierarchyPath_exp_integrable (n+1) m f hf x g hg hb)] | |
| apply integral_congr_ae | |
| filter_upwards [] with z | |
| simp only [hierarchyPathWeight] | |
| have he (y : ℝ) : Real.exp (m (Fin.last n)*(f (z,y)-gaussianStep (m (Fin.last n)) f z)) * | |
| hierarchyPathWeight n (fun i => m i.castSucc) (gaussianStep (m (Fin.last n)) f) z*g (z,y) = | |
| hierarchyPathWeight n (fun i => m i.castSucc) (gaussianStep (m (Fin.last n)) f) z * | |
| (Real.exp (m (Fin.last n)*(f (z,y)-gaussianStep (m (Fin.last n)) f z))*g (z,y)) := by ring | |
| simp_rw [he] | |
| rw [integral_const_mul] | |
| congr 1 | |
| simp_rw [gaussianTransition_density hf] | |
| rw [gaussianAverage,gaussianStepLaw_integral_inv_smul] | |
| simp only [smul_eq_mul,div_eq_mul_inv,mul_assoc,← integral_const_mul] | |
| apply integral_congr_ae | |
| filter_upwards [] with y | |
| ring | |
| theorem hierarchyAverage_eq_integral_exp (n : ℕ) (m : Fin n → ℝ) | |
| (f : ParameterSpace n → ℝ) (hf : BoundedDerivs f) (g : ParameterSpace n → ℝ) | |
| (hg : Continuous g) (hb : HasExpGrowth g) (x : ℝ) : | |
| hierarchyAverage n m f g x = ∫ z, g z ∂hierarchyPathLaw n m f x := by | |
| induction n with | |
| | zero => | |
| rw [hierarchyPathLaw_integral 0 m f hf] | |
| simp only [hierarchyAverage,hierarchyPathWeight,one_mul,fiberGaussian,integral_dirac] | |
| | succ n ih => | |
| rw [hierarchyPath_integral_succ_exp n m f hf x g hg hb] | |
| exact ih (fun i => m i.castSucc) _ (hf.gaussianStep _) _ | |
| (gaussianAverage_continuous_of_expGrowth hf _ hb hg) (gaussianAverage_expGrowth hf _ hb) | |
| end SK.Analytic | |
| end | |
| end | |
| end | |
| end OAI | |