English
For AEMeasurable X, Y, and f, the conditional distribution map respects change of variables: condDistrib Y X μ mapped by f equals condDistrib (Y ∘ f) (X ∘ f) ν where ν is μ mapped by f.
Русский
Для AEMеасмерble X, Y и f условное распределение сохраняет изменение переменных: condDistrib Y X μ после отображения f равно condDistrib (Y ∘ f) (X ∘ f) ν, где ν — мера ν.
LaTeX
$$$$ \\mathrm{condDistrib}(Y,X, μ) \\mapsto_f \\mathrm condDistrib (Y\\circ f)(X\\circ f) ν $$$$
Lean4
theorem stronglyMeasurable_integral_condDistrib (hf : StronglyMeasurable f) :
StronglyMeasurable[mβ.comap X] (fun a ↦ ∫ y, f (X a, y) ∂condDistrib Y X μ (X a)) :=
(hf.integral_condDistrib).comp_measurable <| Measurable.of_comap_le le_rfl