Documentation

LeanBandits.ForMathlib.Traj

def MeasurableEquiv.piIicZero (X : Type u_2) [(n : ) → MeasurableSpace (X n)] :
((i : { x : // x Finset.Iic 0 }) → X i) ≃ᵐ X 0

Measurable equivalence between Iic 0 → X i and X 0.

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    noncomputable def ProbabilityTheory.Kernel.trajMeasure {X : Type u_1} [(n : ) → MeasurableSpace (X n)] (μ₀ : MeasureTheory.Measure (X 0)) (κ : (n : ) → Kernel ((i : { x : // x Finset.Iic n }) → X i) (X (n + 1))) [∀ (n : ), IsMarkovKernel (κ n)] :
    MeasureTheory.Measure ((n : ) → X n)

    Distribution of the infinite trajectory given the distribution of X 0.

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      instance ProbabilityTheory.Kernel.instIsProbabilityMeasureForallTrajMeasure {X : Type u_1} [(n : ) → MeasurableSpace (X n)] {κ : (n : ) → Kernel ((i : { x : // x Finset.Iic n }) → X i) (X (n + 1))} [∀ (n : ), IsMarkovKernel (κ n)] {μ₀ : MeasureTheory.Measure (X 0)} [MeasureTheory.IsProbabilityMeasure μ₀] :
      theorem ProbabilityTheory.Kernel.traj_map_eq_kernel {X : Type u_1} [(n : ) → MeasurableSpace (X n)] {κ : (n : ) → Kernel ((i : { x : // x Finset.Iic n }) → X i) (X (n + 1))} [∀ (n : ), IsMarkovKernel (κ n)] {a : } :
      ((traj κ a).map fun (x : (n : ) → X n) => x (a + 1)) = κ a
      theorem ProbabilityTheory.Kernel.partialTraj_compProd_kernel_eq_traj_map {X : Type u_1} [(n : ) → MeasurableSpace (X n)] {κ : (n : ) → Kernel ((i : { x : // x Finset.Iic n }) → X i) (X (n + 1))} [∀ (n : ), IsMarkovKernel (κ n)] {a : } {x₀ : (n : { x : // x Finset.Iic 0 }) → X n} :
      ((partialTraj κ 0 a) x₀).compProd (κ a) = MeasureTheory.Measure.map (fun (x : (i : ) → 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 : Type u_1} [(n : ) → MeasurableSpace (X n)] {κ : (n : ) → Kernel ((i : { x : // x Finset.Iic n }) → X i) (X (n + 1))} [∀ (n : ), IsMarkovKernel (κ n)] {μ₀ : MeasureTheory.Measure (X 0)} [MeasureTheory.IsProbabilityMeasure μ₀] {a : } :
      theorem ProbabilityTheory.Kernel.condDistrib_trajMeasure_ae_eq_kernel {X : Type u_1} [(n : ) → MeasurableSpace (X n)] {κ : (n : ) → Kernel ((i : { x : // x Finset.Iic n }) → X i) (X (n + 1))} [∀ (n : ), IsMarkovKernel (κ n)] {μ₀ : MeasureTheory.Measure (X 0)} [MeasureTheory.IsProbabilityMeasure μ₀] {a : } [StandardBorelSpace (X (a + 1))] [Nonempty (X (a + 1))] :
      (condDistrib (fun (x : (i : ) → 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 : Type u_1} [(n : ) → MeasurableSpace (X n)] {κ : (n : ) → Kernel ((i : { x : // x Finset.Iic n }) → X i) (X (n + 1))} [∀ (n : ), IsMarkovKernel (κ n)] :
      ((traj κ 0).map fun (h : (n : ) → X n) => h 0) = deterministic (MeasurableEquiv.piIicZero X)