ProbabilityTheory.Kernel.hasLaw_eval_zero_trajMeasureFin
Lemma
No docstring.
Given
-
X : ℕ → Type u_2(n : ℕ) → MeasurableSpace (X n)A measurable space is a space equipped with a σ-algebra. -
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.
Then
HasLaw (fun x => x 0) ((κ' 0) default) (trajMeasureFin κ')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.MeasurableSpace : Type u_6 → Type u_6A measurable space is a space equipped with a σ-algebra.
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.
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`.
Inhabited.default : {α : Sort u} → [self : Inhabited α] → α`default` is a function that produces a "default" element of any `Inhabited` type. This element does not have any particular specified properties, but it is often an all-zeroes value.
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_eval_zero_trajMeasureFin :
HasLaw (fun x ↦ x 0) (κ' 0 default) (trajMeasureFin κ')Proof
hasLaw_eval_zero_trajMeasure
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, 41 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.