Learning.hasCondDistrib_unit
From the authors
A random variable with values in Unit admits any Markov kernel as conditional
distribution.
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Ω : Type u_4mΩ : MeasurableSpace ΩA measurable space is a space equipped with a σ-algebra. -
α : Type u_5mα : MeasurableSpace α
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P : MeasureTheory.Measure ΩA measure is defined to be an outer measure that is countably additive on measurable sets, with the additional assumption that the outer measure is the canonical extension of the restricted measure.MeasureTheory.IsProbabilityMeasure PA measureμis called a probability measure ifμ univ = 1. -
X : Ω → α -
U : Ω → Unit -
κ : ProbabilityTheory.Kernel α UnitA kernel from a measurable spaceαto another measurable spaceβis a measurable functionκ : α → Measure β.ProbabilityTheory.IsMarkovKernel κA kernel is a Markov kernel if every measure in its image is a probability measure.
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hX : AEMeasurable X PA function is almost everywhere measurable if it coincides almost everywhere with a measurable function.
ProbabilityTheory.HasCondDistrib U X κ PY given X under the measure P is equal to the kernel κ.MeasurableSpace : Type u_6 → Type u_6A measurable space is a space equipped with a σ-algebra.
MeasureTheory.IsProbabilityMeasure : {α : Type u_1} → {m0 : MeasurableSpace α} → MeasureTheory.Measure α → PropA measure `μ` is called a probability measure if `μ univ = 1`.
MeasureTheory.Measure : (α : Type u_5) → [MeasurableSpace α] → Type u_5A measure is defined to be an outer measure that is countably additive on measurable sets, with the additional assumption that the outer measure is the canonical extension of the restricted measure. The measure of a set `s`, denoted `μ s`, is an extended nonnegative real. The real-valued version is written `μ.real s`.
Unit : TypeThe canonical type with one element. This element is written `()`. `Unit` has a number of uses: * It can be used to model control flow that returns from a function call without providing other information. * Monadic actions that return `Unit` have side effects without computing values. * In polymorphic types, it can be used to indicate that no data is to be stored in a particular field.
ProbabilityTheory.IsMarkovKernel : {α : Type u_1} →
{β : Type u_2} → {mα : MeasurableSpace α} → {mβ : MeasurableSpace β} → ProbabilityTheory.Kernel α β → PropA kernel is a Markov kernel if every measure in its image is a probability measure.
ProbabilityTheory.Kernel : (α : Type u_1) → (β : Type u_2) → [MeasurableSpace α] → [MeasurableSpace β] → Type (max u_1 u_2)A kernel from a measurable space `α` to another measurable space `β` is a measurable function `κ : α → Measure β`. The measurable space structure on `MeasureTheory.Measure β` is given by `MeasureTheory.Measure.instMeasurableSpace`. A map `κ : α → MeasureTheory.Measure β` is measurable iff `∀ s : Set β, MeasurableSet s → Measurable (fun a ↦ κ a s)`.
AEMeasurable : {α : Type u_1} →
{β : Type u_2} →
[MeasurableSpace β] →
{_m : MeasurableSpace α} → (α → β) → autoParam (MeasureTheory.Measure α) AEMeasurable._auto_1 → PropA function is almost everywhere measurable if it coincides almost everywhere with a measurable function. A similar notion is `MeasureTheory.NullMeasurable`. That notion is equivalent to `AEMeasurable` if the σ-algebra on the codomain is countably generated, but weaker in general.
ProbabilityTheory.HasCondDistrib : {Ω : Type u_1} →
{𝓧 : Type u_2} →
{𝓨 : Type u_3} →
{mΩ : MeasurableSpace Ω} →
{m𝓧 : MeasurableSpace 𝓧} →
{m𝓨 : MeasurableSpace 𝓨} → (Ω → 𝓨) → (Ω → 𝓧) → ProbabilityTheory.Kernel 𝓧 𝓨 → MeasureTheory.Measure Ω → PropPredicate stating that the conditional distribution of `Y` given `X` under the measure `P` is equal to the kernel `κ`.
Code
lemma hasCondDistrib_unit {α : Type*} {mα : MeasurableSpace α} {P : Measure Ω}
[IsProbabilityMeasure P] {X : Ω → α} (hX : AEMeasurable X P) (U : Ω → Unit)
(κ : Kernel α Unit) [IsMarkovKernel κ] :
HasCondDistrib U X κ PProof
by
have hU : U = fun _ ↦ () := funext fun _ ↦ rfl
subst hU
refine HasLaw.mk (hX.prodMk aemeasurable_const) ?_
rw [Kernel.eq_const_dirac_unit κ, Measure.compProd_const, Measure.prod_dirac,
AEMeasurable.map_map_of_aemeasurable (by fun_prop) hX]
rflMeaning last changed in v4.34.0-rc2-76-g565f652 (2026-09-10).
Self-contained, with its dependencies inlined and proofs replaced by sorry: download the raw file · open it in the Lean web editor.
Dependency graph
Nothing to draw. Its statement rests on no other declaration in this project, and names nothing from a package left unaudited — so the graph is this declaration alone. That is the answer, not a missing picture.
Audit surface: 0 project declarations, 9 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.