Measurable equivalence between Iic 0 → X i
and X 0
.
Equations
- MeasurableEquiv.piIicZero X = (MeasurableEquiv.piUnique fun (i : { x : ℕ // x ∈ Finset.Iic 0 }) => X ↑i).trans (MeasurableEquiv.piIicZero._proof_1 ▸ MeasurableEquiv.refl (X 0))
Instances For
noncomputable def
ProbabilityTheory.Kernel.trajMeasure
{X : ℕ → Type u_1}
[(n : ℕ) → MeasurableSpace (X n)]
(μ₀ : MeasureTheory.Measure (X 0))
(κ : (n : ℕ) → Kernel ((i : { x : ℕ // x ∈ Finset.Iic n }) → X ↑i) (X (n + 1)))
[∀ (n : ℕ), IsMarkovKernel (κ n)]
:
MeasureTheory.Measure ((n : ℕ) → X n)
Distribution of the infinite trajectory given the distribution of X 0
.
Equations
Instances For
instance
ProbabilityTheory.Kernel.instIsProbabilityMeasureForallTrajMeasure
{X : ℕ → Type u_1}
[(n : ℕ) → MeasurableSpace (X n)]
{κ : (n : ℕ) → Kernel ((i : { x : ℕ // x ∈ Finset.Iic n }) → X ↑i) (X (n + 1))}
[∀ (n : ℕ), IsMarkovKernel (κ n)]
{μ₀ : MeasureTheory.Measure (X 0)}
[MeasureTheory.IsProbabilityMeasure μ₀]
:
theorem
ProbabilityTheory.Kernel.partialTraj_compProd_kernel_eq_traj_map
{X : ℕ → Type u_1}
[(n : ℕ) → MeasurableSpace (X n)]
{κ : (n : ℕ) → Kernel ((i : { x : ℕ // x ∈ Finset.Iic n }) → X ↑i) (X (n + 1))}
[∀ (n : ℕ), IsMarkovKernel (κ n)]
{a : ℕ}
{x₀ : (n : { x : ℕ // x ∈ Finset.Iic 0 }) → X ↑n}
:
((partialTraj κ 0 a) x₀).compProd (κ a) = MeasureTheory.Measure.map (fun (x : (i : ℕ) → X i) => (Preorder.frestrictLe a x, x (a + 1))) ((traj κ 0) x₀)
theorem
ProbabilityTheory.Kernel.trajMeasure_map_frestrictLe_compProd_kernel_eq_trajMeasure_map
{X : ℕ → Type u_1}
[(n : ℕ) → MeasurableSpace (X n)]
{κ : (n : ℕ) → Kernel ((i : { x : ℕ // x ∈ Finset.Iic n }) → X ↑i) (X (n + 1))}
[∀ (n : ℕ), IsMarkovKernel (κ n)]
{μ₀ : MeasureTheory.Measure (X 0)}
[MeasureTheory.IsProbabilityMeasure μ₀]
{a : ℕ}
:
(MeasureTheory.Measure.map (Preorder.frestrictLe a) (trajMeasure μ₀ κ)).compProd (κ a) = MeasureTheory.Measure.map (fun (x : (i : ℕ) → X i) => (Preorder.frestrictLe a x, x (a + 1))) (trajMeasure μ₀ κ)
theorem
ProbabilityTheory.Kernel.condDistrib_trajMeasure_ae_eq_kernel
{X : ℕ → Type u_1}
[(n : ℕ) → MeasurableSpace (X n)]
{κ : (n : ℕ) → Kernel ((i : { x : ℕ // x ∈ Finset.Iic n }) → X ↑i) (X (n + 1))}
[∀ (n : ℕ), IsMarkovKernel (κ n)]
{μ₀ : MeasureTheory.Measure (X 0)}
[MeasureTheory.IsProbabilityMeasure μ₀]
{a : ℕ}
[StandardBorelSpace (X (a + 1))]
[Nonempty (X (a + 1))]
:
⇑(condDistrib (fun (x : (i : ℕ) → X i) => x (a + 1)) (Preorder.frestrictLe a)
(trajMeasure μ₀ κ)) =ᵐ[MeasureTheory.Measure.map (Preorder.frestrictLe a) (trajMeasure μ₀ κ)] ⇑(κ a)
theorem
ProbabilityTheory.Kernel.traj_zero_map_eval_zero
{X : ℕ → Type u_1}
[(n : ℕ) → MeasurableSpace (X n)]
{κ : (n : ℕ) → Kernel ((i : { x : ℕ // x ∈ Finset.Iic n }) → X ↑i) (X (n + 1))}
[∀ (n : ℕ), IsMarkovKernel (κ n)]
: