LeanMachineLearning

Bandits.ArrayModel.indepFun_fst_auxπŸ”—

Lemma

No docstring.

Types
  • 𝓐 : Type u_1m𝓐 : MeasurableSpace 𝓐A measurable space is a space equipped with a Οƒ-algebra.
  • 𝓑 : Type u_2m𝓑 : MeasurableSpace 𝓑
Given
  • Ξ½ : ProbabilityTheory.Kernel 𝓐 𝓑A kernel from a measurable space Ξ± to another measurable space Ξ² is a measurable function ΞΊ : Ξ± β†’ Measure Ξ².ProbabilityTheory.IsMarkovKernel Ξ½A kernel is a Markov kernel if every measure in its image is a probability measure.
  • n : β„•
Then
ProbabilityTheory.IndepFun (fun Ο‰ => Ο‰.1 n) (fun Ο‰ => (fun i => Ο‰.1 ↑i, Ο‰.2)) (arrayMeasure Ξ½)
Two functions are independent if the two measurable space structures they generate are independent.
Code
lemma indepFun_fst_aux (Ξ½ : Kernel 𝓐 𝓑) [IsMarkovKernel Ξ½] (n : β„•) :
    (fun Ο‰ ↦ Ο‰.1 n) βŸ‚α΅’[arrayMeasure Ξ½] (fun Ο‰ ↦ (fun (i : Fin n) ↦ Ο‰.1 i, Ο‰.2))
Proof
by
  have h : IndepFun (fun Ο‰ : β„• β†’ I ↦ Ο‰ n) (fun Ο‰ (i : Fin n) ↦ Ο‰ i)
      (Measure.infinitePi fun _ ↦ volume) := by
    refine (iIndepFun_infinitePi fun _ ↦ measurable_id).indepFun_of_measurable_iSup_comap
      (fun _ ↦ measurable_pi_apply _) (S := Set.Iio n) (by simp) ?_
    rw [measurable_iff_comap_le, MeasurableSpace.comap_pi]
    exact iSup_le fun i ↦ le_iSupβ‚‚_of_le (i : β„•) (Set.mem_Iio.2 i.2) le_rfl
  exact h.fst_prod (Ξ½ := streamMeasure Ξ½) (by fun_prop) (by fun_prop)

Meaning last changed in v4.35.0-rc2-1-g61e506b (2026-09-22), the 3th recorded change.

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

Dependency graph

Audit surface: 3 project declarations, 29 external constants

βœ“ 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.