Download lean/OAI/Probability/InvariantIsing/Arrays/CountableWardFunctional.lean from roseking/openAI-math-dataset: direct link, hf CLI and curl.
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| import OAI.Probability.InvariantIsing.Arrays.CountableReplicaRestriction | |
| namespace OAI | |
| /-! Bounded two- and three-replica Ward terms pass through leaf exhaustion. | |
| A finite Ward bound is preserved in the countable-leaf limit. -/ | |
| noncomputable section | |
| open MeasureTheory IsingPerceptron Filter | |
| open scoped Topology | |
| namespace InvariantIsing | |
| def expectedReplicaWard {Ω X : Type*} [MeasurableSpace Ω] [MeasurableSpace X] | |
| (P : Measure Ω) (ν : Measure X) (H : Ω × X → ℝ) | |
| (D₂ : Ω × (Fin 2 → X) → ℝ) (D₃ : Ω × (Fin 3 → X) → ℝ) : ℝ := | |
| (∫ ω, referenceReplicaMean ν (fun x => H (ω,x)) (fun σ => D₂ (ω,σ)) ∂P) - | |
| 2 * ∫ ω, referenceReplicaMean ν (fun x => H (ω,x)) (fun σ => D₃ (ω,σ)) ∂P | |
| theorem expectedReplicaWard_restriction_tendsto {Ω X : Type*} | |
| [MeasurableSpace Ω] [MeasurableSpace X] [Countable X] [MeasurableSingletonClass X] | |
| (P : Measure Ω) [IsProbabilityMeasure P] (ν : Measure X) [IsProbabilityMeasure ν] | |
| (H : Ω × X → ℝ) (hH : Measurable H) | |
| (he : ∀ᵐ ω ∂P, Integrable (fun x => Real.exp (H (ω,x))) ν) | |
| (D₂ : Ω × (Fin 2 → X) → ℝ) (hD₂ : Measurable D₂) | |
| (D₃ : Ω × (Fin 3 → X) → ℝ) (hD₃ : Measurable D₃) | |
| {B₂ B₃ : ℝ} (hB₂ : 0 ≤ B₂) (hB₃ : 0 ≤ B₃) | |
| (hb₂ : ∀ z, |D₂ z| ≤ B₂) (hb₃ : ∀ z, |D₃ z| ≤ B₃) | |
| {S : ℕ → Set X} (hS : ∀ n, MeasurableSet (S n)) | |
| (hexh : ∀ x, ∀ᶠ n in atTop, x ∈ S n) (hpos : ∀ n, ν (S n) ≠ 0) : | |
| Tendsto (fun n => expectedReplicaWard P (normalizedRestriction ν (S n)) H D₂ D₃) atTop | |
| (𝓝 (expectedReplicaWard P ν H D₂ D₃)) := by | |
| exact (expected_referenceReplicaMean_restriction_tendsto P ν H hH he D₂ hD₂ hB₂ hb₂ | |
| hS hexh hpos).sub | |
| ((expected_referenceReplicaMean_restriction_tendsto P ν H hH he D₃ hD₃ hB₃ hb₃ | |
| hS hexh hpos).const_mul 2) | |
| /-- Uniform finite Ward residual bounds are preserved for the full countable | |
| Gibbs prior. No convergence or integrability of a Gaussian correction is needed. -/ | |
| theorem expectedReplicaWard_abs_le_of_restriction {Ω X : Type*} | |
| [MeasurableSpace Ω] [MeasurableSpace X] [Countable X] [MeasurableSingletonClass X] | |
| (P : Measure Ω) [IsProbabilityMeasure P] (ν : Measure X) [IsProbabilityMeasure ν] | |
| (H : Ω × X → ℝ) (hH : Measurable H) | |
| (he : ∀ᵐ ω ∂P, Integrable (fun x => Real.exp (H (ω,x))) ν) | |
| (D₂ : Ω × (Fin 2 → X) → ℝ) (hD₂ : Measurable D₂) | |
| (D₃ : Ω × (Fin 3 → X) → ℝ) (hD₃ : Measurable D₃) | |
| {B₂ B₃ : ℝ} (hB₂ : 0 ≤ B₂) (hB₃ : 0 ≤ B₃) | |
| (hb₂ : ∀ z, |D₂ z| ≤ B₂) (hb₃ : ∀ z, |D₃ z| ≤ B₃) | |
| {S : ℕ → Set X} (hS : ∀ n, MeasurableSet (S n)) | |
| (hexh : ∀ x, ∀ᶠ n in atTop, x ∈ S n) (hpos : ∀ n, ν (S n) ≠ 0) | |
| {ε : ℝ} (hfinite : ∀ n, | |
| |expectedReplicaWard P (normalizedRestriction ν (S n)) H D₂ D₃| ≤ ε) : | |
| |expectedReplicaWard P ν H D₂ D₃| ≤ ε := by | |
| exact le_of_tendsto (expectedReplicaWard_restriction_tendsto P ν H hH he | |
| D₂ hD₂ D₃ hD₃ hB₂ hB₃ hb₂ hb₃ hS hexh hpos).abs | |
| (Filter.Eventually.of_forall hfinite) | |
| end InvariantIsing | |
| end | |
| end OAI | |