Regular conditional probability distribution #
We define the regular conditional probability distribution of Y : α → Ω given X : α → β, where
Ω is a standard Borel space. This is a Kernel β Ω such that for almost all a, condDistrib
evaluated at X a and a measurable set s is equal to the conditional expectation
μ⟦Y ⁻¹' s | mβ.comap X⟧ evaluated at a.
μ⟦Y ⁻¹' s | mβ.comap X⟧ maps a measurable set s to a function α → ℝ≥0∞, and for all s that
map is unique up to a μ-null set. For all a, the map from sets to ℝ≥0∞ that we obtain that way
verifies some of the properties of a measure, but in general the fact that the μ-null set depends
on s can prevent us from finding versions of the conditional expectation that combine into a true
measure. The standard Borel space assumption on Ω allows us to do so.
The case Y = X = id is developed in more detail in Probability/Kernel/Condexp.lean: here X is
understood as a map from Ω with a sub-σ-algebra m to Ω with its default σ-algebra and the
conditional distribution defines a kernel associated with the conditional expectation with respect
to m.
Main definitions #
- condDistrib Y X μ: regular conditional probability distribution of- Y : α → Ωgiven- X : α → β, where- Ωis a standard Borel space.
Main statements #
- condDistrib_ae_eq_condExp: for almost all- a,- condDistribevaluated at- X aand a measurable set- sis equal to the conditional expectation- μ⟦Y ⁻¹' s | mβ.comap X⟧ a.
- condExp_prod_ae_eq_integral_condDistrib: the conditional expectation- μ[(fun a => f (X a, Y a)) | X; mβ]is almost everywhere equal to the integral- ∫ y, f (X a, y) ∂(condDistrib Y X μ (X a)).
Regular conditional probability distribution: kernel associated with the conditional
expectation of Y given X.
For almost all a, condDistrib Y X μ evaluated at X a and a measurable set s is equal to
the conditional expectation μ⟦Y ⁻¹' s | mβ.comap X⟧ a. It also satisfies the equality
μ[(fun a => f (X a, Y a)) | mβ.comap X] =ᵐ[μ] fun a => ∫ y, f (X a, y) ∂(condDistrib Y X μ (X a))
for all integrable functions f.
Equations
- ProbabilityTheory.condDistrib Y X μ = (MeasureTheory.Measure.map (fun (a : α) => (X a, Y a)) μ).condKernel
Instances For
If the singleton {x} has non-zero mass for μ.map X, then for all s : Set Ω,
condDistrib Y X μ x s = (μ.map X {x})⁻¹ * μ.map (fun a => (X a, Y a)) ({x} ×ˢ s) .
condDistrib is a.e. uniquely defined as the kernel satisfying the defining property of
condKernel.
condDistrib is a.e. uniquely defined as the kernel satisfying the defining property of
condKernel.
For almost every a : α, the condDistrib Y X μ kernel applied to X a and a measurable set
s is equal to the conditional expectation of the indicator of Y ⁻¹' s.
The conditional expectation of a function f of the product (X, Y) is almost everywhere equal
to the integral of y ↦ f(X, y) against the condDistrib kernel.
The conditional expectation of a function f of the product (X, Y) is almost everywhere equal
to the integral of y ↦ f(X, y) against the condDistrib kernel.
The conditional expectation of a function f of the product (X, Y) is almost everywhere equal
to the integral of y ↦ f(X, y) against the condDistrib kernel.
The conditional expectation of Y given X is almost everywhere equal to the integral
∫ y, y ∂(condDistrib Y X μ (X a)).
Alias of MeasureTheory.AEStronglyMeasurable.comp_snd_map_prodMk.
Alias of ProbabilityTheory.aestronglyMeasurable_comp_snd_map_prodMk_iff.
Alias of ProbabilityTheory.integrable_comp_snd_map_prodMk_iff.