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 ofY : α → ΩgivenX : α → β, whereΩis a standard Borel space.
Main statements #
condDistrib_ae_eq_condExp: for almost alla,condDistribevaluated atX aand a measurable setsis equal to the conditional expectationμ⟦Y ⁻¹' s | mβ.comap X⟧ a.condExp_prod_ae_eq_integral_condDistrib: the conditional expectationμ[(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) .
Alias of ProbabilityTheory.aestronglyMeasurable_integral_condDistrib.
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.
Alias of ProbabilityTheory.condDistrib_ae_eq_condExp.
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.
Alias of ProbabilityTheory.condExp_prod_ae_eq_integral_condDistrib'.
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.
Alias of ProbabilityTheory.condExp_prod_ae_eq_integral_condDistrib₀.
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.
Alias of ProbabilityTheory.condExp_prod_ae_eq_integral_condDistrib.
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.
Alias of ProbabilityTheory.condExp_ae_eq_integral_condDistrib.
The conditional expectation of Y given X is almost everywhere equal to the integral
∫ y, y ∂(condDistrib Y X μ (X a)).
Alias of ProbabilityTheory.condExp_ae_eq_integral_condDistrib'.
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.
Alias of ProbabilityTheory.condExp_ae_eq_integral_condDistrib_id.