LeanMachineLearning

ProbabilityTheory.Kernel.monoComp₀🔗

Definition

The kernelized version of the monoidal composition of kernels using the SFinKer category. It uses arbitrary measurable equivalences to transport the kernels to the SFinKer category.

🔗def
ProbabilityTheory.Kernel.monoComp₀.{u_1, u_2, u_3, u_4, u_5}
  {W : Type u_1} {X : Type u_2} {Y : Type u_3} {Z : Type u_4}
  [MeasurableSpace W] [MeasurableSpace X] [MeasurableSpace Y]
  [MeasurableSpace Z] {SW SX SY SZ : SFinKer}
  (ew : SFinKer.carrier SW ≃ᵐ W) (ex : SFinKer.carrier SX ≃ᵐ X)
  (ey : SFinKer.carrier SY ≃ᵐ Y) (ez : SFinKer.carrier SZ ≃ᵐ Z)
  [MeasurableCoherence X Y] (κ : Kernel W X) [IsSFiniteKernel κ]
  (η : Kernel Y Z) [IsSFiniteKernel η] : Kernel W Z
ProbabilityTheory.Kernel.monoComp₀.{u_1,
    u_2, u_3, u_4, u_5}
  {W : Type u_1} {X : Type u_2}
  {Y : Type u_3} {Z : Type u_4}
  [MeasurableSpace W] [MeasurableSpace X]
  [MeasurableSpace Y] [MeasurableSpace Z]
  {SW SX SY SZ : SFinKer}
  (ew : SFinKer.carrier SW ≃ᵐ W)
  (ex : SFinKer.carrier SX ≃ᵐ X)
  (ey : SFinKer.carrier SY ≃ᵐ Y)
  (ez : SFinKer.carrier SZ ≃ᵐ Z)
  [MeasurableCoherence X Y]
  (κ : Kernel W X) [IsSFiniteKernel κ]
  (η : Kernel Y Z) [IsSFiniteKernel η] :
  Kernel W Z

Code

noncomputable def monoComp₀ : Kernel W Z := have := monoidalCoherence ex ey fromHom (ex := ew) (ey := ez) <| hom (ex := ew) (ey := ex) κ ⊗≫ hom (ex := ey) (ey := ez) η

Actions: Source · Open Issue

Meaning last changed in v4.34.0-rc2-1-g439785b (2026-08-23).

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: 4 project declarations, 25 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.