ProbabilityTheory.Kernel.hasLaw_trajMeasureFin
Lemma
From the authors
Uniqueness of trajMeasureFin.
Types
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Ω : Type u_1mΩ : MeasurableSpace ΩA measurable space is a space equipped with a σ-algebra.
Given
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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 : ℕ → Type u_2(n : ℕ) → MeasurableSpace (X n) -
κ' : (n : ℕ) → Kernel ((i : Fin n) → X ↑i) (X n)A kernel from a measurable spaceαto another measurable spaceβis a measurable functionκ : α → Measure β.∀ (n : ℕ), IsMarkovKernel (κ' n)A kernel is a Markov kernel if every measure in its image is a probability measure. -
Y : (n : ℕ) → Ω → X n
Assuming
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hY_meas : ∀ (n : ℕ), Measurable (Y n)A functionfbetween measurable spaces is measurable if the preimage of every measurable set is measurable. -
h_condDistrib : ∀ (n : ℕ), HasCondDistrib (Y n) (fun ω i => Y (↑i) ω) (κ' n) PPredicate stating that the conditional distribution ofYgivenXunder the measurePis equal to the kernelκ.
Then
HasLaw (fun ω n => Y n ω) (trajMeasureFin κ') PHasLaw X μ P registers the fact that the random variable X has law μ under the measure P, in other words that P.map X = μ.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`.
Nat : TypeThe natural numbers, starting at zero. This type is special-cased by both the kernel and the compiler, and overridden with an efficient implementation. Both use a fast arbitrary-precision arithmetic library (usually [GMP](https://gmplib.org/)); at runtime, `Nat` values that are sufficiently small are unboxed.
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)`.
Fin : ℕ → TypeNatural numbers less than some upper bound. In particular, a `Fin n` is a natural number `i` with the constraint that `i < n`. It is the canonical type with `n` elements.
Measurable : {α : Type u_1} → {β : Type u_2} → [MeasurableSpace α] → [MeasurableSpace β] → (α → β) → PropA function `f` between measurable spaces is measurable if the preimage of every measurable set is measurable.
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`.
ProbabilityTheory.HasLaw : {Ω : Type u_1} →
{𝓧 : Type u_2} →
{mΩ : MeasurableSpace Ω} →
{m𝓧 : MeasurableSpace 𝓧} →
(Ω → 𝓧) → MeasureTheory.Measure 𝓧 → autoParam (MeasureTheory.Measure Ω) ProbabilityTheory.HasLaw._auto_1 → PropThe predicate `HasLaw X μ P` registers the fact that the random variable `X` has law `μ` under the measure `P`, in other words that `P.map X = μ`. We also require `X` to be `AEMeasurable`, to allow for nice interactions with operations on the codomain of `X`. See for instance `HasLaw.comp`, `IndepFun.hasLaw_mul` and `IndepFun.hasLaw_add`.
ProbabilityTheory.Kernel.trajMeasureFin : {X : ℕ → Type u_2} →
[inst : (n : ℕ) → MeasurableSpace (X n)] →
(κ' : (n : ℕ) → ProbabilityTheory.Kernel ((i : Fin n) → X ↑i) (X n)) →
[∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ' n)] → MeasureTheory.Measure ((n : ℕ) → X n)Measure on trajectories `Π n, X n` built from kernels `κ' n : Kernel (Π i : Fin n, X i) (X n)` describing the law of the coordinate `n` given the `n` previous coordinates. The initial measure is `κ' 0 default`.Go to its page
Code
lemma hasLaw_trajMeasureFin [IsProbabilityMeasure P]
{Y : (n : ℕ) → Ω → X n} (hY_meas : ∀ n, Measurable (Y n))
(h_condDistrib : ∀ n, HasCondDistrib (Y n) (fun ω (i : Fin n) ↦ Y i ω) (κ' n) P) :
HasLaw (fun ω n ↦ Y n ω) (trajMeasureFin κ') PProof
by
unfold trajMeasureFin
refine hasLaw_trajMeasure hY_meas ?_ fun n ↦ ?_
· have h := h_condDistrib 0
rw [show (fun ω (i : Fin 0) ↦ Y i ω) = fun _ ↦ default from
funext fun _ ↦ Unique.eq_default _] at h
exact h.hasLaw_of_const'
· exact (h_condDistrib (n + 1)).measurableEquiv_comp_right (MeasurableEquiv.finSuccPiIic X n)Meaning 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
Audit surface: 3 project declarations, 43 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.