`Learning.IsBayesAlgEnvSeq.integrable_uncurry_actionMean_comp`🔗

Lemma

No docstring.

theorem

Learning.IsBayesAlgEnvSeq.integrable_uncurry_actionMean_comp.{u_1, u_2,
    u_4}
  {𝓔 : Type u_1} {𝓐 : Type u_2} {Ω : Type u_4} [MeasurableSpace 𝓔]
  [MeasurableSpace 𝓐] [MeasurableSpace Ω] [Countable 𝓐]
  [MeasurableSingletonClass 𝓐] {κ : ProbabilityTheory.Kernel (𝓔 × 𝓐) ℝ}
  {E : Ω → 𝓔} (hE : Measurable E) {f : Ω → 𝓐} (hf : Measurable f)
  {P : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure P]
  {l u : ℝ}
  (hm : ∀ (e : 𝓔) (a : 𝓐), ∫ (x : ℝ), id x ∂κ (e, a) ∈ Set.Icc l u) :
  MeasureTheory.Integrable (fun ω => actionMean κ E (f ω) ω) P
Learning.IsBayesAlgEnvSeq.integrable_uncurry_actionMean_comp.{u_1,
    u_2, u_4}
  {𝓔 : Type u_1} {𝓐 : Type u_2}
  {Ω : Type u_4} [MeasurableSpace 𝓔]
  [MeasurableSpace 𝓐] [MeasurableSpace Ω]
  [Countable 𝓐]
  [MeasurableSingletonClass 𝓐]
  {κ : ProbabilityTheory.Kernel (𝓔 × 𝓐) ℝ}
  {E : Ω → 𝓔} (hE : Measurable E)
  {f : Ω → 𝓐} (hf : Measurable f)
  {P : MeasureTheory.Measure Ω}
  [MeasureTheory.IsFiniteMeasure P]
  {l u : ℝ}
  (hm :
    ∀ (e : 𝓔) (a : 𝓐),
      ∫ (x : ℝ), id x ∂κ (e, a) ∈
        Set.Icc l u) :
  MeasureTheory.Integrable
    (fun ω => actionMean κ E (f ω) ω) P

Code

lemma integrable_uncurry_actionMean_comp [Countable 𝓐] [MeasurableSingletonClass 𝓐]
    {κ : Kernel (𝓔 × 𝓐) ℝ} {E : Ω → 𝓔} (hE : Measurable E) {f : Ω → 𝓐} (hf : Measurable f)
    {P : Measure Ω} [IsFiniteMeasure P] {l u : ℝ} (hm : ∀ e a, (κ (e, a))[id] ∈ (Set.Icc l u)) :
    Integrable (fun ω ↦ actionMean κ E (f ω) ω) P

Proof

by
  refine ⟨(measurable_uncurry_actionMean_comp hE hf).aestronglyMeasurable, ?_⟩
  apply HasFiniteIntegral.of_bounded
  filter_upwards with ω using abs_le_max_abs_abs (hm (E ω) (f ω)).1 (hm (E ω) (f ω)).2

Actions: Source · Open Issue

Meaning last changed in v4.34.0-rc2-1-g439785b (2026-08-23), the 2th recorded change.

Self-contained, with its dependencies inlined and proofs replaced by sorry: download the raw file · open it in the Lean web editor.

Dependency graph

Audit surface: 1 project declarations, 37 external constants

✓ Proved: no sorry anywhere in its closure

This is the tool's own reading of one build's recorded axioms, and it is not robust against an author who wants it to pass. Checking meant to be relied on should go through Comparator, which replays the proof through the kernel from an export against an explicit list of permitted axioms.

Learning.IsBayesAlgEnvSeq.integrable_uncurry_actionMean_comp🔗

`Learning.IsBayesAlgEnvSeq.integrable_uncurry_actionMean_comp`🔗