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Found 155 declarations mentioning ProbabilityTheory.IsMarkovKernel.
- ProbabilityTheory.IsMarkovKernel 📋 Mathlib.Probability.Kernel.Defs
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} (κ : ProbabilityTheory.Kernel α β) : Prop - ProbabilityTheory.Kernel.instIsMarkovKernelOfIsEmpty 📋 Mathlib.Probability.Kernel.Defs
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} [IsEmpty α] (κ : ProbabilityTheory.Kernel α β) : ProbabilityTheory.IsMarkovKernel κ - ProbabilityTheory.IsMarkovKernel.IsZeroOrMarkovKernel 📋 Mathlib.Probability.Kernel.Defs
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {κ : ProbabilityTheory.Kernel α β} [h : ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.IsZeroOrMarkovKernel κ - ProbabilityTheory.Kernel.not_isMarkovKernel_zero 📋 Mathlib.Probability.Kernel.Defs
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} [Nonempty α] : ¬ProbabilityTheory.IsMarkovKernel 0 - ProbabilityTheory.Kernel.bound_eq_one 📋 Mathlib.Probability.Kernel.Defs
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} [Nonempty α] (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] : κ.bound = 1 - ProbabilityTheory.IsMarkovKernel.isProbabilityMeasure 📋 Mathlib.Probability.Kernel.Defs
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {κ : ProbabilityTheory.Kernel α β} [self : ProbabilityTheory.IsMarkovKernel κ] (a : α) : MeasureTheory.IsProbabilityMeasure (κ a) - ProbabilityTheory.IsMarkovKernel.is_probability_measure' 📋 Mathlib.Probability.Kernel.Defs
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {κ : ProbabilityTheory.Kernel α β} [ProbabilityTheory.IsMarkovKernel κ] (a : α) : MeasureTheory.IsProbabilityMeasure (κ a) - ProbabilityTheory.IsMarkovKernel.mk 📋 Mathlib.Probability.Kernel.Defs
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {κ : ProbabilityTheory.Kernel α β} (isProbabilityMeasure : ∀ (a : α), MeasureTheory.IsProbabilityMeasure (κ a)) : ProbabilityTheory.IsMarkovKernel κ - ProbabilityTheory.eq_zero_or_isMarkovKernel 📋 Mathlib.Probability.Kernel.Defs
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} (κ : ProbabilityTheory.Kernel α β) [h : ProbabilityTheory.IsZeroOrMarkovKernel κ] : κ = 0 ∨ ProbabilityTheory.IsMarkovKernel κ - ProbabilityTheory.IsZeroOrMarkovKernel.eq_zero_or_isMarkovKernel' 📋 Mathlib.Probability.Kernel.Defs
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {κ : ProbabilityTheory.Kernel α β} [self : ProbabilityTheory.IsZeroOrMarkovKernel κ] : κ = 0 ∨ ProbabilityTheory.IsMarkovKernel κ - ProbabilityTheory.IsZeroOrMarkovKernel.mk 📋 Mathlib.Probability.Kernel.Defs
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {κ : ProbabilityTheory.Kernel α β} (eq_zero_or_isMarkovKernel' : κ = 0 ∨ ProbabilityTheory.IsMarkovKernel κ) : ProbabilityTheory.IsZeroOrMarkovKernel κ - ProbabilityTheory.Kernel.instIsMarkovKernelId 📋 Mathlib.Probability.Kernel.Basic
{α : Type u_1} {mα : MeasurableSpace α} : ProbabilityTheory.IsMarkovKernel ProbabilityTheory.Kernel.id - ProbabilityTheory.Kernel.instIsMarkovKernelPUnitDiscard 📋 Mathlib.Probability.Kernel.Basic
{α : Type u_1} {mα : MeasurableSpace α} : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.discard α) - ProbabilityTheory.Kernel.instIsMarkovKernelProdCopy 📋 Mathlib.Probability.Kernel.Basic
{α : Type u_1} {mα : MeasurableSpace α} : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.copy α) - ProbabilityTheory.Kernel.const.instIsMarkovKernel 📋 Mathlib.Probability.Kernel.Basic
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {μβ : MeasureTheory.Measure β} [hμβ : MeasureTheory.IsProbabilityMeasure μβ] : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.const α μβ) - ProbabilityTheory.Kernel.instNonemptySubtypeIsMarkovKernel 📋 Mathlib.Probability.Kernel.Basic
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} [Nonempty β] : Nonempty { κ // ProbabilityTheory.IsMarkovKernel κ } - ProbabilityTheory.Kernel.isMarkovKernel_deterministic 📋 Mathlib.Probability.Kernel.Basic
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {f : α → β} (hf : Measurable f) : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.deterministic f hf) - ProbabilityTheory.Kernel.instIsMarkovKernelBoolBoolKernelOfIsProbabilityMeasure 📋 Mathlib.Probability.Kernel.Basic
{α : Type u_1} {mα : MeasurableSpace α} {μ ν : MeasureTheory.Measure α} [MeasureTheory.IsProbabilityMeasure μ] [MeasureTheory.IsProbabilityMeasure ν] : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.boolKernel μ ν) - ProbabilityTheory.Kernel.instIsMarkovKernelProdSwap 📋 Mathlib.Probability.Kernel.Basic
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.swap α β) - ProbabilityTheory.Kernel.IsMarkovKernel.piecewise 📋 Mathlib.Probability.Kernel.Basic
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {κ η : ProbabilityTheory.Kernel α β} {s : Set α} {hs : MeasurableSet s} [DecidablePred fun x => x ∈ s] [ProbabilityTheory.IsMarkovKernel κ] [ProbabilityTheory.IsMarkovKernel η] : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.piecewise hs κ η) - ProbabilityTheory.Kernel.IsMarkovKernel.comapRight 📋 Mathlib.Probability.Kernel.Basic
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {γ : Type u_4} {mγ : MeasurableSpace γ} {f : γ → β} (κ : ProbabilityTheory.Kernel α β) (hf : MeasurableEmbedding f) (hκ : ∀ (a : α), (κ a) (Set.range f) = 1) : ProbabilityTheory.IsMarkovKernel (κ.comapRight hf) - ProbabilityTheory.Kernel.exists_ae_eq_isMarkovKernel 📋 Mathlib.Probability.Kernel.Basic
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {κ : ProbabilityTheory.Kernel α β} {μ : MeasureTheory.Measure α} (h : ∀ᵐ (a : α) ∂μ, MeasureTheory.IsProbabilityMeasure (κ a)) (h' : μ ≠ 0) : ∃ η, ⇑κ =ᵐ[μ] ⇑η ∧ ProbabilityTheory.IsMarkovKernel η - ProbabilityTheory.Kernel.comp_discard 📋 Mathlib.Probability.Kernel.Composition.Comp
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] : (ProbabilityTheory.Kernel.discard β).comp κ = ProbabilityTheory.Kernel.discard α - ProbabilityTheory.Kernel.IsMarkovKernel.comp 📋 Mathlib.Probability.Kernel.Composition.Comp
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} (η : ProbabilityTheory.Kernel β γ) [ProbabilityTheory.IsMarkovKernel η] (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.IsMarkovKernel (η.comp κ) - ProbabilityTheory.Kernel.const_comp' 📋 Mathlib.Probability.Kernel.Composition.Comp
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} (μ : MeasureTheory.Measure γ) (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] : (ProbabilityTheory.Kernel.const β μ).comp κ = ProbabilityTheory.Kernel.const α μ - ProbabilityTheory.Kernel.IsMarkovKernel.prodMkLeft 📋 Mathlib.Probability.Kernel.Composition.MapComap
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.prodMkLeft γ κ) - ProbabilityTheory.Kernel.IsMarkovKernel.prodMkRight 📋 Mathlib.Probability.Kernel.Composition.MapComap
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.prodMkRight γ κ) - ProbabilityTheory.Kernel.IsMarkovKernel.map 📋 Mathlib.Probability.Kernel.Composition.MapComap
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {γ : Type u_4} {mγ : MeasurableSpace γ} {f : β → γ} (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] (hf : Measurable f) : ProbabilityTheory.IsMarkovKernel (κ.map f) - ProbabilityTheory.Kernel.IsMarkovKernel.comap 📋 Mathlib.Probability.Kernel.Composition.MapComap
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {γ : Type u_4} {mγ : MeasurableSpace γ} {g : γ → α} (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] (hg : Measurable g) : ProbabilityTheory.IsMarkovKernel (κ.comap g hg) - ProbabilityTheory.Kernel.IsMarkovKernel.fst 📋 Mathlib.Probability.Kernel.Composition.MapComap
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel α (β × γ)) [ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.IsMarkovKernel κ.fst - ProbabilityTheory.Kernel.IsMarkovKernel.snd 📋 Mathlib.Probability.Kernel.Composition.MapComap
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel α (β × γ)) [ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.IsMarkovKernel κ.snd - ProbabilityTheory.Kernel.instIsMarkovKernelProdOfSectL 📋 Mathlib.Probability.Kernel.Composition.MapComap
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {γ : Type u_4} {mγ : MeasurableSpace γ} {κ : ProbabilityTheory.Kernel (α × β) γ} [∀ (b : β), ProbabilityTheory.IsMarkovKernel (κ.sectL b)] : ProbabilityTheory.IsMarkovKernel κ - ProbabilityTheory.Kernel.instIsMarkovKernelProdOfSectR 📋 Mathlib.Probability.Kernel.Composition.MapComap
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {γ : Type u_4} {mγ : MeasurableSpace γ} {κ : ProbabilityTheory.Kernel (α × β) γ} [∀ (b : α), ProbabilityTheory.IsMarkovKernel (κ.sectR b)] : ProbabilityTheory.IsMarkovKernel κ - ProbabilityTheory.Kernel.instIsMarkovKernelSectLOfProd 📋 Mathlib.Probability.Kernel.Composition.MapComap
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {γ : Type u_4} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel (α × β) γ) (b : β) [ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.IsMarkovKernel (κ.sectL b) - ProbabilityTheory.Kernel.instIsMarkovKernelSectROfProd 📋 Mathlib.Probability.Kernel.Composition.MapComap
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {γ : Type u_4} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel (α × β) γ) (a : α) [ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.IsMarkovKernel (κ.sectR a) - ProbabilityTheory.Kernel.IsMarkovKernel.swapLeft 📋 Mathlib.Probability.Kernel.Composition.MapComap
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel (α × β) γ) [ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.IsMarkovKernel κ.swapLeft - ProbabilityTheory.Kernel.IsMarkovKernel.swapRight 📋 Mathlib.Probability.Kernel.Composition.MapComap
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel α (β × γ)) [ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.IsMarkovKernel κ.swapRight - ProbabilityTheory.Kernel.instIsMarkovKernelProdParallelComp 📋 Mathlib.Probability.Kernel.Composition.ParallelComp
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {δ : Type u_4} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} {mδ : MeasurableSpace δ} {κ : ProbabilityTheory.Kernel α β} {η : ProbabilityTheory.Kernel γ δ} [ProbabilityTheory.IsMarkovKernel κ] [ProbabilityTheory.IsMarkovKernel η] : ProbabilityTheory.IsMarkovKernel (κ.parallelComp η) - ProbabilityTheory.Kernel.IsMarkovKernel.compProd 📋 Mathlib.Probability.Kernel.Composition.CompProd
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] (η : ProbabilityTheory.Kernel (α × β) γ) [ProbabilityTheory.IsMarkovKernel η] : ProbabilityTheory.IsMarkovKernel (κ.compProd η) - ProbabilityTheory.Kernel.fst_compProd 📋 Mathlib.Probability.Kernel.Composition.CompProd
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel α β) (η : ProbabilityTheory.Kernel (α × β) γ) [ProbabilityTheory.IsSFiniteKernel κ] [ProbabilityTheory.IsMarkovKernel η] : (κ.compProd η).fst = κ - ProbabilityTheory.Kernel.compProd_apply_univ 📋 Mathlib.Probability.Kernel.Composition.CompProd
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} {κ : ProbabilityTheory.Kernel α β} {η : ProbabilityTheory.Kernel (α × β) γ} [ProbabilityTheory.IsSFiniteKernel κ] [ProbabilityTheory.IsMarkovKernel η] {a : α} : ((κ.compProd η) a) Set.univ = (κ a) Set.univ - ProbabilityTheory.Kernel.compProd_preimage_fst 📋 Mathlib.Probability.Kernel.Composition.CompProd
{α : Type u_1} {β : Type u_2} {γ : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ} {s : Set β} (hs : MeasurableSet s) (κ : ProbabilityTheory.Kernel α β) (η : ProbabilityTheory.Kernel (α × β) γ) [ProbabilityTheory.IsSFiniteKernel κ] [ProbabilityTheory.IsMarkovKernel η] (x : α) : ((κ.compProd η) x) (Prod.fst ⁻¹' s) = (κ x) s - MeasureTheory.Measure.instIsProbabilityMeasureProdCompProdOfIsMarkovKernel 📋 Mathlib.Probability.Kernel.Composition.MeasureCompProd
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {μ : MeasureTheory.Measure α} {κ : ProbabilityTheory.Kernel α β} [MeasureTheory.IsProbabilityMeasure μ] [ProbabilityTheory.IsMarkovKernel κ] : MeasureTheory.IsProbabilityMeasure (μ.compProd κ) - MeasureTheory.Measure.fst_compProd 📋 Mathlib.Probability.Kernel.Composition.MeasureCompProd
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} (μ : MeasureTheory.Measure α) [MeasureTheory.SFinite μ] (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] : (μ.compProd κ).fst = μ - MeasureTheory.Measure.compProd_apply_univ 📋 Mathlib.Probability.Kernel.Composition.MeasureCompProd
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {μ : MeasureTheory.Measure α} {κ : ProbabilityTheory.Kernel α β} [MeasureTheory.SFinite μ] [ProbabilityTheory.IsMarkovKernel κ] : (μ.compProd κ) Set.univ = μ Set.univ - MeasureTheory.Measure.comp_apply_univ 📋 Mathlib.Probability.Kernel.Composition.CompNotation
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {μ : MeasureTheory.Measure α} {κ : ProbabilityTheory.Kernel α β} [ProbabilityTheory.IsMarkovKernel κ] : (μ.bind ⇑κ) Set.univ = μ Set.univ - ProbabilityTheory.Kernel.IsMarkovKernel.prod 📋 Mathlib.Probability.Kernel.Composition.Prod
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {γ : Type u_4} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] (η : ProbabilityTheory.Kernel α γ) [ProbabilityTheory.IsMarkovKernel η] : ProbabilityTheory.IsMarkovKernel (κ.prod η) - ProbabilityTheory.Kernel.fst_prod 📋 Mathlib.Probability.Kernel.Composition.Prod
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {γ : Type u_4} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsSFiniteKernel κ] (η : ProbabilityTheory.Kernel α γ) [ProbabilityTheory.IsMarkovKernel η] : (κ.prod η).fst = κ - ProbabilityTheory.Kernel.snd_prod 📋 Mathlib.Probability.Kernel.Composition.Prod
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {γ : Type u_4} {mγ : MeasurableSpace γ} (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] (η : ProbabilityTheory.Kernel α γ) [ProbabilityTheory.IsSFiniteKernel η] : (κ.prod η).snd = η - MeasureTheory.Measure.instIsProbabilityMeasureBindCoeKernelOfIsMarkovKernel 📋 Mathlib.Probability.Kernel.Composition.MeasureComp
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {μ : MeasureTheory.Measure α} {κ : ProbabilityTheory.Kernel α β} [MeasureTheory.IsProbabilityMeasure μ] [ProbabilityTheory.IsMarkovKernel κ] : MeasureTheory.IsProbabilityMeasure (μ.bind ⇑κ) - ConvexOn.integrable_apply_rnDeriv_of_integrable_compProd 📋 Mathlib.MeasureTheory.Measure.Decomposition.IntegralRNDeriv
{𝓧 : Type u_1} {m𝓧 : MeasurableSpace 𝓧} {μ ν : MeasureTheory.Measure 𝓧} {𝓨 : Type u_2} {m𝓨 : MeasurableSpace 𝓨} {κ η : ProbabilityTheory.Kernel 𝓧 𝓨} {f : ℝ → ℝ} [MeasureTheory.IsFiniteMeasure μ] [MeasureTheory.IsFiniteMeasure ν] [ProbabilityTheory.IsMarkovKernel κ] [ProbabilityTheory.IsMarkovKernel η] (hf : MeasureTheory.StronglyMeasurable f) (hf_cvx : ConvexOn ℝ (Set.Ici 0) f) (hf_cont_at : ContinuousWithinAt f (Set.Ici 0) 0) (hf_int : MeasureTheory.Integrable (fun p => f ((μ.compProd κ).rnDeriv (ν.compProd η) p).toReal) (ν.compProd η)) (hκη : (μ.compProd κ).AbsolutelyContinuous (μ.compProd η)) : MeasureTheory.Integrable (fun a => f (μ.rnDeriv ν a).toReal) ν - ConvexOn.apply_rnDeriv_ae_le_integral 📋 Mathlib.MeasureTheory.Measure.Decomposition.IntegralRNDeriv
{𝓧 : Type u_1} {m𝓧 : MeasurableSpace 𝓧} {μ ν : MeasureTheory.Measure 𝓧} {𝓨 : Type u_2} {m𝓨 : MeasurableSpace 𝓨} {κ η : ProbabilityTheory.Kernel 𝓧 𝓨} {f : ℝ → ℝ} [MeasureTheory.IsFiniteMeasure μ] [MeasureTheory.IsFiniteMeasure ν] [ProbabilityTheory.IsMarkovKernel κ] [ProbabilityTheory.IsMarkovKernel η] (hf : MeasureTheory.StronglyMeasurable f) (hf_cvx : ConvexOn ℝ (Set.Ici 0) f) (hf_cont_at : ContinuousWithinAt f (Set.Ici 0) 0) (h_int : MeasureTheory.Integrable (fun p => f ((μ.compProd κ).rnDeriv (ν.compProd η) p).toReal) (ν.compProd η)) (hκη : (μ.compProd κ).AbsolutelyContinuous (μ.compProd η)) : (fun a => f (μ.rnDeriv ν a).toReal) ≤ᵐ[ν] fun a => ∫ (b : 𝓨), f ((μ.compProd κ).rnDeriv (ν.compProd η) (a, b)).toReal ∂η a - InformationTheory.klDiv_compProd_left 📋 Mathlib.InformationTheory.KullbackLeibler.ChainRule
{𝓧 : Type u_1} {𝓨 : Type u_2} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} (μ ν : MeasureTheory.Measure 𝓧) (κ : ProbabilityTheory.Kernel 𝓧 𝓨) [MeasureTheory.IsFiniteMeasure μ] [MeasureTheory.IsFiniteMeasure ν] [ProbabilityTheory.IsMarkovKernel κ] : InformationTheory.klDiv (μ.compProd κ) (ν.compProd κ) = InformationTheory.klDiv μ ν - InformationTheory.klDiv_compProd_eq_add 📋 Mathlib.InformationTheory.KullbackLeibler.ChainRule
{𝓧 : Type u_1} {𝓨 : Type u_2} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} (μ ν : MeasureTheory.Measure 𝓧) (κ η : ProbabilityTheory.Kernel 𝓧 𝓨) [MeasureTheory.IsFiniteMeasure μ] [MeasureTheory.IsFiniteMeasure ν] [ProbabilityTheory.IsMarkovKernel κ] [ProbabilityTheory.IsMarkovKernel η] : InformationTheory.klDiv (μ.compProd κ) (ν.compProd η) = InformationTheory.klDiv μ ν + InformationTheory.klDiv (μ.compProd κ) (μ.compProd η) - InformationTheory.integrable_llr_of_integrable_llr_compProd 📋 Mathlib.InformationTheory.KullbackLeibler.ChainRule
{𝓧 : Type u_1} {𝓨 : Type u_2} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {μ ν : MeasureTheory.Measure 𝓧} {κ η : ProbabilityTheory.Kernel 𝓧 𝓨} [MeasureTheory.IsFiniteMeasure μ] [MeasureTheory.IsFiniteMeasure ν] [ProbabilityTheory.IsMarkovKernel κ] [ProbabilityTheory.IsMarkovKernel η] (h_ac : (μ.compProd κ).AbsolutelyContinuous (ν.compProd η)) (h_int : MeasureTheory.Integrable (MeasureTheory.llr (μ.compProd κ) (ν.compProd η)) (μ.compProd κ)) : MeasureTheory.Integrable (MeasureTheory.llr μ ν) μ - InformationTheory.integrable_llr_compProd_iff 📋 Mathlib.InformationTheory.KullbackLeibler.ChainRule
{𝓧 : Type u_1} {𝓨 : Type u_2} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {μ ν : MeasureTheory.Measure 𝓧} {κ η : ProbabilityTheory.Kernel 𝓧 𝓨} [MeasureTheory.IsFiniteMeasure μ] [MeasureTheory.IsFiniteMeasure ν] [ProbabilityTheory.IsMarkovKernel κ] [ProbabilityTheory.IsMarkovKernel η] (h_ac : (μ.compProd κ).AbsolutelyContinuous (ν.compProd η)) : MeasureTheory.Integrable (MeasureTheory.llr (μ.compProd κ) (ν.compProd η)) (μ.compProd κ) ↔ MeasureTheory.Integrable (MeasureTheory.llr μ ν) μ ∧ MeasureTheory.Integrable (MeasureTheory.llr (μ.compProd κ) (μ.compProd η)) (μ.compProd κ) - InformationTheory.rnDeriv_compProd_mul_log_eq_mul_add 📋 Mathlib.InformationTheory.KullbackLeibler.ChainRule
{𝓧 : Type u_1} {𝓨 : Type u_2} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {μ ν : MeasureTheory.Measure 𝓧} {κ η : ProbabilityTheory.Kernel 𝓧 𝓨} [MeasureTheory.IsFiniteMeasure μ] [MeasureTheory.IsFiniteMeasure ν] [ProbabilityTheory.IsMarkovKernel κ] [ProbabilityTheory.IsMarkovKernel η] (h_ac : (μ.compProd κ).AbsolutelyContinuous (μ.compProd η)) : ∀ᵐ (p : 𝓧 × 𝓨) ∂ν.compProd η, ((μ.compProd κ).rnDeriv (ν.compProd η) p).toReal * Real.log ((μ.compProd κ).rnDeriv (ν.compProd η) p).toReal = ((μ.compProd κ).rnDeriv (ν.compProd η) p).toReal * (Real.log (μ.rnDeriv ν p.1).toReal + Real.log ((μ.compProd κ).rnDeriv (μ.compProd η) p).toReal) - InformationTheory.integral_llr_compProd_eq_add 📋 Mathlib.InformationTheory.KullbackLeibler.ChainRule
{𝓧 : Type u_1} {𝓨 : Type u_2} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {μ ν : MeasureTheory.Measure 𝓧} {κ η : ProbabilityTheory.Kernel 𝓧 𝓨} [MeasureTheory.IsFiniteMeasure μ] [MeasureTheory.IsFiniteMeasure ν] [ProbabilityTheory.IsMarkovKernel κ] [ProbabilityTheory.IsMarkovKernel η] (h_ac : (μ.compProd κ).AbsolutelyContinuous (ν.compProd η)) (h_int : MeasureTheory.Integrable (MeasureTheory.llr (μ.compProd κ) (ν.compProd η)) (μ.compProd κ)) : ∫ (p : 𝓧 × 𝓨), MeasureTheory.llr (μ.compProd κ) (ν.compProd η) p ∂μ.compProd κ = ∫ (a : 𝓧), MeasureTheory.llr μ ν a ∂μ + ∫ (p : 𝓧 × 𝓨), MeasureTheory.llr (μ.compProd κ) (μ.compProd η) p ∂μ.compProd κ - InformationTheory.klDiv_comp_right_le 📋 Mathlib.InformationTheory.KullbackLeibler.DataProcessing
{𝓧 : Type u_1} {𝓨 : Type u_2} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} (μ ν : MeasureTheory.Measure 𝓧) [MeasureTheory.IsFiniteMeasure μ] [MeasureTheory.IsFiniteMeasure ν] (κ : ProbabilityTheory.Kernel 𝓧 𝓨) [ProbabilityTheory.IsMarkovKernel κ] : InformationTheory.klDiv (μ.bind ⇑κ) (ν.bind ⇑κ) ≤ InformationTheory.klDiv μ ν - ProbabilityTheory.Kernel.iIndep.of_subsingleton 📋 Mathlib.Probability.Independence.Kernel.Indep
{α : Type u_1} {Ω : Type u_2} {ι : Type u_3} {_mα : MeasurableSpace α} {_mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure α} [Subsingleton ι] {m : ι → MeasurableSpace Ω} {κ : ProbabilityTheory.Kernel α Ω} [ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.Kernel.iIndep m κ μ - ProbabilityTheory.Kernel.iIndepSets.of_subsingleton 📋 Mathlib.Probability.Independence.Kernel.Indep
{α : Type u_1} {Ω : Type u_2} {ι : Type u_3} {_mα : MeasurableSpace α} {_mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure α} [Subsingleton ι] {m : ι → Set (Set Ω)} {κ : ProbabilityTheory.Kernel α Ω} [ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.Kernel.iIndepSets m κ μ - ProbabilityTheory.Kernel.iIndepFun.of_subsingleton 📋 Mathlib.Probability.Independence.Kernel.IndepFun
{α : Type u_1} {Ω : Type u_2} {ι : Type u_3} {_mα : MeasurableSpace α} {_mΩ : MeasurableSpace Ω} {κ : ProbabilityTheory.Kernel α Ω} {μ : MeasureTheory.Measure α} [Subsingleton ι] {β : ι → Type u_10} {m : (i : ι) → MeasurableSpace (β i)} {f : (i : ι) → Ω → β i} [ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.Kernel.iIndepFun f κ μ - ProbabilityTheory.instIsMarkovKernel_toKernel 📋 Mathlib.Probability.Kernel.Disintegration.CDFToKernel
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {x✝ : MeasurableSpace β} {f : α × β → StieltjesFunction ℝ} {κ : ProbabilityTheory.Kernel α (β × ℝ)} {ν : ProbabilityTheory.Kernel α β} {hf : ProbabilityTheory.IsCondKernelCDF f κ ν} : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.IsCondKernelCDF.toKernel f hf) - ProbabilityTheory.Kernel.condKernelCountable.instIsMarkovKernel 📋 Mathlib.Probability.Kernel.Disintegration.Basic
{α : Type u_1} {β : Type u_2} {Ω : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mΩ : MeasurableSpace Ω} [Countable α] (κCond : α → ProbabilityTheory.Kernel β Ω) [∀ (a : α), ProbabilityTheory.IsMarkovKernel (κCond a)] (h_atom : ∀ (x y : α), x ∈ measurableAtom y → κCond x = κCond y) : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.condKernelCountable κCond h_atom) - MeasureTheory.Measure.IsCondKernel.isMarkovKernel 📋 Mathlib.Probability.Kernel.Disintegration.Basic
{α : Type u_1} {Ω : Type u_3} {mα : MeasurableSpace α} {mΩ : MeasurableSpace Ω} (ρ : MeasureTheory.Measure (α × Ω)) (ρCond : ProbabilityTheory.Kernel α Ω) [ρ.IsCondKernel ρCond] [MeasureTheory.IsFiniteMeasure ρ] [MeasurableSingletonClass α] (hρ : ∀ (a : α), ρ.fst {a} ≠ 0) : ProbabilityTheory.IsMarkovKernel ρCond - ProbabilityTheory.Kernel.condKernelCountable.instIsCondKernel 📋 Mathlib.Probability.Kernel.Disintegration.Basic
{α : Type u_1} {β : Type u_2} {Ω : Type u_3} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mΩ : MeasurableSpace Ω} [Countable α] (κCond : α → ProbabilityTheory.Kernel β Ω) [∀ (a : α), ProbabilityTheory.IsMarkovKernel (κCond a)] (h_atom : ∀ (x y : α), x ∈ measurableAtom y → κCond x = κCond y) (κ : ProbabilityTheory.Kernel α (β × Ω)) [ProbabilityTheory.IsSFiniteKernel κ] [∀ (a : α), (κ a).IsCondKernel (κCond a)] : κ.IsCondKernel (ProbabilityTheory.Kernel.condKernelCountable κCond h_atom) - ProbabilityTheory.Kernel.instIsMarkovKernelBorelMarkovFromReal 📋 Mathlib.Probability.Kernel.Disintegration.StandardBorel
{α : Type u_1} {Ω : Type u_4} {mα : MeasurableSpace α} {mΩ : MeasurableSpace Ω} [StandardBorelSpace Ω] [Nonempty Ω] (η : ProbabilityTheory.Kernel α ℝ) [ProbabilityTheory.IsMarkovKernel η] : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.borelMarkovFromReal Ω η) - ProbabilityTheory.Kernel.instIsMarkovKernelCondKernelUnitReal 📋 Mathlib.Probability.Kernel.Disintegration.StandardBorel
{α : Type u_1} {mα : MeasurableSpace α} (κ : ProbabilityTheory.Kernel Unit (α × ℝ)) [ProbabilityTheory.IsFiniteKernel κ] : ProbabilityTheory.IsMarkovKernel κ.condKernelUnitReal - MeasureTheory.Measure.instIsMarkovKernelCondKernel 📋 Mathlib.Probability.Kernel.Disintegration.StandardBorel
{α : Type u_1} {Ω : Type u_4} {mα : MeasurableSpace α} {mΩ : MeasurableSpace Ω} [StandardBorelSpace Ω] [Nonempty Ω] (ρ : MeasureTheory.Measure (α × Ω)) [MeasureTheory.IsFiniteMeasure ρ] : ProbabilityTheory.IsMarkovKernel ρ.condKernel - ProbabilityTheory.Kernel.instIsMarkovKernelCondKernelReal 📋 Mathlib.Probability.Kernel.Disintegration.StandardBorel
{α : Type u_1} {γ : Type u_3} {mα : MeasurableSpace α} {mγ : MeasurableSpace γ} [MeasurableSpace.CountablyGenerated γ] (κ : ProbabilityTheory.Kernel α (γ × ℝ)) [ProbabilityTheory.IsFiniteKernel κ] : ProbabilityTheory.IsMarkovKernel κ.condKernelReal - ProbabilityTheory.Kernel.instIsMarkovKernelCondKernelUnitBorel 📋 Mathlib.Probability.Kernel.Disintegration.StandardBorel
{α : Type u_1} {Ω : Type u_4} {mα : MeasurableSpace α} {mΩ : MeasurableSpace Ω} [StandardBorelSpace Ω] [Nonempty Ω] (κ : ProbabilityTheory.Kernel Unit (α × Ω)) [ProbabilityTheory.IsFiniteKernel κ] : ProbabilityTheory.IsMarkovKernel κ.condKernelUnitBorel - ProbabilityTheory.Kernel.instIsMarkovKernelCondKernelBorel 📋 Mathlib.Probability.Kernel.Disintegration.StandardBorel
{α : Type u_1} {γ : Type u_3} {Ω : Type u_4} {mα : MeasurableSpace α} {mγ : MeasurableSpace γ} [MeasurableSpace.CountablyGenerated γ] {mΩ : MeasurableSpace Ω} [StandardBorelSpace Ω] [Nonempty Ω] (κ : ProbabilityTheory.Kernel α (γ × Ω)) [ProbabilityTheory.IsFiniteKernel κ] : ProbabilityTheory.IsMarkovKernel κ.condKernelBorel - ProbabilityTheory.Kernel.instIsMarkovKernelCondKernel 📋 Mathlib.Probability.Kernel.Disintegration.StandardBorel
{α : Type u_1} {β : Type u_2} {Ω : Type u_4} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {mΩ : MeasurableSpace Ω} [StandardBorelSpace Ω] [Nonempty Ω] [h : MeasurableSpace.CountableOrCountablyGenerated α β] (κ : ProbabilityTheory.Kernel α (β × Ω)) [ProbabilityTheory.IsFiniteKernel κ] : ProbabilityTheory.IsMarkovKernel κ.condKernel - ProbabilityTheory.condKernel_compProd 📋 Mathlib.Probability.Kernel.Disintegration.Unique
{α : Type u_1} {Ω : Type u_3} {mα : MeasurableSpace α} [MeasurableSpace Ω] [StandardBorelSpace Ω] [Nonempty Ω] (μ : MeasureTheory.Measure α) [MeasureTheory.IsFiniteMeasure μ] (κ : ProbabilityTheory.Kernel α Ω) [ProbabilityTheory.IsMarkovKernel κ] : ⇑(μ.compProd κ).condKernel =ᵐ[μ] ⇑κ - ProbabilityTheory.instIsMarkovKernelCondDistrib 📋 Mathlib.Probability.Kernel.CondDistrib
{α : Type u_1} {β : Type u_2} {Ω : Type u_3} [MeasurableSpace Ω] [StandardBorelSpace Ω] [Nonempty Ω] {mα : MeasurableSpace α} {μ : MeasureTheory.Measure α} [MeasureTheory.IsFiniteMeasure μ] {X : α → β} {Y : α → Ω} [MeasurableSpace β] : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.condDistrib Y X μ) - DependsOn.lmarginalPartialTraj_of_le 📋 Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj
{X : ℕ → Type u_1} {mX : (n : ℕ) → MeasurableSpace (X n)} {a b : ℕ} {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (c : ℕ) {f : ((n : ℕ) → X n) → ENNReal} (mf : Measurable f) (hf : DependsOn f ↑(Finset.Iic a)) (hab : a ≤ b) : ProbabilityTheory.Kernel.lmarginalPartialTraj κ b c f = f - DependsOn.lmarginalPartialTraj_const_right 📋 Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj
{X : ℕ → Type u_1} {mX : (n : ℕ) → MeasurableSpace (X n)} {a b c : ℕ} {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {d : ℕ} {f : ((n : ℕ) → X n) → ENNReal} (mf : Measurable f) (hf : DependsOn f ↑(Finset.Iic a)) (hac : a ≤ c) (had : a ≤ d) : ProbabilityTheory.Kernel.lmarginalPartialTraj κ b c f = ProbabilityTheory.Kernel.lmarginalPartialTraj κ b d f - ProbabilityTheory.Kernel.instIsMarkovKernelForallValNatMemFinsetIicPartialTrajOfHAddOfNat 📋 Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj
{X : ℕ → Type u_1} {mX : (n : ℕ) → MeasurableSpace (X n)} {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a b : ℕ) : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.partialTraj κ a b) - ProbabilityTheory.Kernel.partialTraj_comp_partialTraj' 📋 Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj
{X : ℕ → Type u_1} {mX : (n : ℕ) → MeasurableSpace (X n)} {a b : ℕ} {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (c : ℕ) (hab : a ≤ b) : (ProbabilityTheory.Kernel.partialTraj κ b c).comp (ProbabilityTheory.Kernel.partialTraj κ a b) = ProbabilityTheory.Kernel.partialTraj κ a c - ProbabilityTheory.Kernel.partialTraj_comp_partialTraj'' 📋 Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj
{X : ℕ → Type u_1} {mX : (n : ℕ) → MeasurableSpace (X n)} {a : ℕ} {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {b c : ℕ} (hcb : c ≤ b) : (ProbabilityTheory.Kernel.partialTraj κ b c).comp (ProbabilityTheory.Kernel.partialTraj κ a b) = ProbabilityTheory.Kernel.partialTraj κ a c - ProbabilityTheory.Kernel.partialTraj_map_frestrictLe₂ 📋 Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj
{X : ℕ → Type u_1} {mX : (n : ℕ) → MeasurableSpace (X n)} {b c : ℕ} {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a : ℕ) (hbc : b ≤ c) : (ProbabilityTheory.Kernel.partialTraj κ a c).map (Preorder.frestrictLe₂ hbc) = ProbabilityTheory.Kernel.partialTraj κ a b - ProbabilityTheory.Kernel.partialTraj_succ_map_frestrictLe₂ 📋 Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj
{X : ℕ → Type u_1} {mX : (n : ℕ) → MeasurableSpace (X n)} {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a b : ℕ) : (ProbabilityTheory.Kernel.partialTraj κ a (b + 1)).map (Preorder.frestrictLe₂ ⋯) = ProbabilityTheory.Kernel.partialTraj κ a b - ProbabilityTheory.Kernel.map_partialTraj_succ_self 📋 Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj
{X : ℕ → Type u_1} {mX : (n : ℕ) → MeasurableSpace (X n)} {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a : ℕ) : ((ProbabilityTheory.Kernel.partialTraj κ a (a + 1)).map fun x => x ⟨a + 1, ⋯⟩) = κ a - ProbabilityTheory.Kernel.partialTraj_map_frestrictLe₂_apply 📋 Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj
{X : ℕ → Type u_1} {mX : (n : ℕ) → MeasurableSpace (X n)} {a b c : ℕ} {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) (hbc : b ≤ c) : MeasureTheory.Measure.map (Preorder.frestrictLe₂ hbc) ((ProbabilityTheory.Kernel.partialTraj κ a c) x₀) = (ProbabilityTheory.Kernel.partialTraj κ a b) x₀ - ProbabilityTheory.Kernel.IsMarkovKernel.integrable 📋 Mathlib.Probability.Kernel.Integral
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} (μ : MeasureTheory.Measure α) [MeasureTheory.IsFiniteMeasure μ] (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] {s : Set β} (hs : MeasurableSet s) : MeasureTheory.Integrable (fun x => (κ x).real s) μ - ProbabilityTheory.Kernel.trajMeasure 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] (μ₀ : MeasureTheory.Measure (X 0)) (κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))) [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] : MeasureTheory.Measure ((n : ℕ) → X n) - ProbabilityTheory.Kernel.instIsProbabilityMeasureForallTrajMeasure 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {μ₀ : MeasureTheory.Measure (X 0)} [MeasureTheory.IsProbabilityMeasure μ₀] : MeasureTheory.IsProbabilityMeasure (ProbabilityTheory.Kernel.trajMeasure μ₀ κ) - ProbabilityTheory.Kernel.trajFun 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] (κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))) [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a : ℕ) (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) : MeasureTheory.Measure ((n : ℕ) → X n) - ProbabilityTheory.Kernel.trajContent 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] (κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))) [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a : ℕ} (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) : MeasureTheory.AddContent ENNReal (MeasureTheory.measurableCylinders X) - ProbabilityTheory.Kernel.isProbabilityMeasure_trajFun 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] (κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))) [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a : ℕ) (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) : MeasureTheory.IsProbabilityMeasure (ProbabilityTheory.Kernel.trajFun κ a x₀) - ProbabilityTheory.Kernel.isSigmaSubadditive_trajContent 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] (κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))) [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a : ℕ} (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) : (ProbabilityTheory.Kernel.trajContent κ x₀).IsSigmaSubadditive - ProbabilityTheory.Kernel.trajContent_ne_top 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a : ℕ} {x : (i : ↥(Finset.Iic a)) → X ↑i} {s : Set ((n : ℕ) → X n)} : (ProbabilityTheory.Kernel.trajContent κ x) s ≠ ⊤ - ProbabilityTheory.Kernel.traj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] (κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))) [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a : ℕ) : ProbabilityTheory.Kernel ((i : ↥(Finset.Iic a)) → X ↑i) ((n : ℕ) → X n) - ProbabilityTheory.Kernel.instIsMarkovKernelForallValNatMemFinsetIicForallTraj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a : ℕ) : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.Kernel.traj κ a) - ProbabilityTheory.Kernel.trajContent_tendsto_zero 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {A : ℕ → Set ((n : ℕ) → X n)} (A_mem : ∀ (n : ℕ), A n ∈ MeasureTheory.measurableCylinders X) (A_anti : Antitone A) (A_inter : ⋂ n, A n = ∅) {p : ℕ} (x₀ : (i : ↥(Finset.Iic p)) → X ↑i) : Filter.Tendsto (fun n => (ProbabilityTheory.Kernel.trajContent κ x₀) (A n)) Filter.atTop (nhds 0) - ProbabilityTheory.Kernel.measurable_trajFun 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a : ℕ) : Measurable (ProbabilityTheory.Kernel.trajFun κ a) - ProbabilityTheory.Kernel.le_lmarginalPartialTraj_succ 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {f : ℕ → ((n : ℕ) → X n) → ENNReal} {a : ℕ → ℕ} (hcte : ∀ (n : ℕ), DependsOn (f n) ↑(Finset.Iic (a n))) (mf : ∀ (n : ℕ), Measurable (f n)) {bound : ENNReal} (fin_bound : bound ≠ ⊤) (le_bound : ∀ (n : ℕ) (x : (n : ℕ) → X n), f n x ≤ bound) {k : ℕ} (anti : ∀ (x : (n : ℕ) → X n), Antitone fun n => ProbabilityTheory.Kernel.lmarginalPartialTraj κ (k + 1) (a n) (f n) x) {l : ((n : ℕ) → X n) → ENNReal} (htendsto : ∀ (x : (n : ℕ) → X n), Filter.Tendsto (fun n => ProbabilityTheory.Kernel.lmarginalPartialTraj κ (k + 1) (a n) (f n) x) Filter.atTop (nhds (l x))) (ε : ENNReal) (y : (i : ↥(Finset.Iic k)) → X ↑i) (hpos : ∀ (x : (i : ℕ) → X i) (n : ℕ), ε ≤ ProbabilityTheory.Kernel.lmarginalPartialTraj κ k (a n) (f n) (Function.updateFinset x (Finset.Iic k) y)) : ∃ z, ∀ (x : (i : ℕ) → X i) (n : ℕ), ε ≤ ProbabilityTheory.Kernel.lmarginalPartialTraj κ (k + 1) (a n) (f n) (Function.update (Function.updateFinset x (Finset.Iic k) y) (k + 1) z) - ProbabilityTheory.Kernel.trajContent_eq_lmarginalPartialTraj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {b : ℕ} {S : Set ((i : ↥(Finset.Iic b)) → X ↑i)} (mS : MeasurableSet S) (x₀ : (n : ℕ) → X n) (a : ℕ) : (ProbabilityTheory.Kernel.trajContent κ (Preorder.frestrictLe a x₀)) (MeasureTheory.cylinder (Finset.Iic b) S) = ProbabilityTheory.Kernel.lmarginalPartialTraj κ a b ((MeasureTheory.cylinder (Finset.Iic b) S).indicator 1) x₀ - ProbabilityTheory.Kernel.map_traj_succ_self 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a : ℕ} : ((ProbabilityTheory.Kernel.traj κ a).map fun x => x (a + 1)) = κ a - ProbabilityTheory.Kernel.traj_apply 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a : ℕ) (x : (i : ↥(Finset.Iic a)) → X ↑i) : (ProbabilityTheory.Kernel.traj κ a) x = ProbabilityTheory.Kernel.trajFun κ a x - ProbabilityTheory.Kernel.traj_comp_partialTraj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a b : ℕ} (hab : a ≤ b) : (ProbabilityTheory.Kernel.traj κ b).comp (ProbabilityTheory.Kernel.partialTraj κ a b) = ProbabilityTheory.Kernel.traj κ a - ProbabilityTheory.Kernel.traj_map_frestrictLe 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a b : ℕ) : (ProbabilityTheory.Kernel.traj κ a).map (Preorder.frestrictLe b) = ProbabilityTheory.Kernel.partialTraj κ a b - ProbabilityTheory.Kernel.isProjectiveMeasureFamily_partialTraj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] (κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))) [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a : ℕ} (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) : MeasureTheory.IsProjectiveMeasureFamily (MeasureTheory.inducedFamily fun b => (ProbabilityTheory.Kernel.partialTraj κ a b) x₀) - ProbabilityTheory.Kernel.isProjectiveLimit_trajFun 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] (κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))) [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a : ℕ) (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) : MeasureTheory.IsProjectiveLimit (ProbabilityTheory.Kernel.trajFun κ a x₀) (MeasureTheory.inducedFamily fun n => (ProbabilityTheory.Kernel.partialTraj κ a n) x₀) - ProbabilityTheory.Kernel.traj_map_updateFinset 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {n : ℕ} (x : (i : ↥(Finset.Iic n)) → X ↑i) : MeasureTheory.Measure.map (fun x_1 => Function.updateFinset x_1 (Finset.Iic n) x) ((ProbabilityTheory.Kernel.traj κ n) x) = (ProbabilityTheory.Kernel.traj κ n) x - ProbabilityTheory.Kernel.lintegral_traj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a : ℕ} (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) {f : ((n : ℕ) → X n) → ENNReal} (mf : Measurable f) : ∫⁻ (x : (n : ℕ) → X n), f x ∂(ProbabilityTheory.Kernel.traj κ a) x₀ = ∫⁻ (x : (n : ℕ) → X n), f (Function.updateFinset x (Finset.Iic a) x₀) ∂(ProbabilityTheory.Kernel.traj κ a) x₀ - ProbabilityTheory.Kernel.map_frestrictLe_trajMeasure_compProd_eq_map_trajMeasure 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {μ₀ : MeasureTheory.Measure (X 0)} [MeasureTheory.IsProbabilityMeasure μ₀] {a : ℕ} : (MeasureTheory.Measure.map (Preorder.frestrictLe a) (ProbabilityTheory.Kernel.trajMeasure μ₀ κ)).compProd (κ a) = MeasureTheory.Measure.map (fun x => (Preorder.frestrictLe a x, x (a + 1))) (ProbabilityTheory.Kernel.trajMeasure μ₀ κ) - ProbabilityTheory.Kernel.eq_traj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] (κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))) [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a : ℕ} (η : ProbabilityTheory.Kernel ((i : ↥(Finset.Iic a)) → X ↑i) ((n : ℕ) → X n)) (hη : ∀ (b : ℕ), η.map (Preorder.frestrictLe b) = ProbabilityTheory.Kernel.partialTraj κ a b) : η = ProbabilityTheory.Kernel.traj κ a - ProbabilityTheory.Kernel.eq_traj' 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] (κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))) [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a : ℕ} (n : ℕ) (η : ProbabilityTheory.Kernel ((i : ↥(Finset.Iic a)) → X ↑i) ((n : ℕ) → X n)) (hη : ∀ b ≥ n, η.map (Preorder.frestrictLe b) = ProbabilityTheory.Kernel.partialTraj κ a b) : η = ProbabilityTheory.Kernel.traj κ a - ProbabilityTheory.Kernel.traj_map_frestrictLe_of_le 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a b : ℕ} (hab : a ≤ b) : (ProbabilityTheory.Kernel.traj κ b).map (Preorder.frestrictLe a) = ProbabilityTheory.Kernel.deterministic (Preorder.frestrictLe₂ hab) ⋯ - ProbabilityTheory.Kernel.lintegral_traj₀ 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a : ℕ} (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) {f : ((n : ℕ) → X n) → ENNReal} (mf : AEMeasurable f ((ProbabilityTheory.Kernel.traj κ a) x₀)) : ∫⁻ (x : (n : ℕ) → X n), f x ∂(ProbabilityTheory.Kernel.traj κ a) x₀ = ∫⁻ (x : (n : ℕ) → X n), f (Function.updateFinset x (Finset.Iic a) x₀) ∂(ProbabilityTheory.Kernel.traj κ a) x₀ - ProbabilityTheory.Kernel.integral_traj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] {a : ℕ} (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) {f : ((n : ℕ) → X n) → E} (mf : MeasureTheory.AEStronglyMeasurable f ((ProbabilityTheory.Kernel.traj κ a) x₀)) : ∫ (x : (n : ℕ) → X n), f x ∂(ProbabilityTheory.Kernel.traj κ a) x₀ = ∫ (x : (n : ℕ) → X n), f (Function.updateFinset x (Finset.Iic a) x₀) ∂(ProbabilityTheory.Kernel.traj κ a) x₀ - ProbabilityTheory.Kernel.integrable_traj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {E : Type u_2} [NormedAddCommGroup E] {a b : ℕ} (hab : a ≤ b) {f : ((n : ℕ) → X n) → E} (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) (i_f : MeasureTheory.Integrable f ((ProbabilityTheory.Kernel.traj κ a) x₀)) : ∀ᵐ (x : (i : ℕ) → X i) ∂(ProbabilityTheory.Kernel.traj κ a) x₀, MeasureTheory.Integrable f ((ProbabilityTheory.Kernel.traj κ b) (Preorder.frestrictLe b x)) - ProbabilityTheory.Kernel.trajContent_cylinder 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a b : ℕ} {S : Set ((i : ↥(Finset.Iic b)) → X ↑i)} (mS : MeasurableSet S) (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) : (ProbabilityTheory.Kernel.trajContent κ x₀) (MeasureTheory.cylinder (Finset.Iic b) S) = ((ProbabilityTheory.Kernel.partialTraj κ a b) x₀) S - ProbabilityTheory.Kernel.traj_map_frestrictLe_apply 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a b : ℕ) (x : (i : ↥(Finset.Iic a)) → X ↑i) : MeasureTheory.Measure.map (Preorder.frestrictLe b) ((ProbabilityTheory.Kernel.traj κ a) x) = (ProbabilityTheory.Kernel.partialTraj κ a b) x - ProbabilityTheory.Kernel.condExp_traj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [CompleteSpace E] {a b : ℕ} (hab : a ≤ b) {x₀ : (i : ↥(Finset.Iic a)) → X ↑i} {f : ((n : ℕ) → X n) → E} (i_f : MeasureTheory.Integrable f ((ProbabilityTheory.Kernel.traj κ a) x₀)) : ((ProbabilityTheory.Kernel.traj κ a) x₀)[f | ↑MeasureTheory.Filtration.piLE b] =ᵐ[(ProbabilityTheory.Kernel.traj κ a) x₀] fun x => ∫ (y : (n : ℕ) → X n), f y ∂(ProbabilityTheory.Kernel.traj κ b) (Preorder.frestrictLe b x) - ProbabilityTheory.Kernel.partialTraj_compProd_traj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a b : ℕ} (hab : a ≤ b) (u : (i : ↥(Finset.Iic a)) → X ↑i) : ((ProbabilityTheory.Kernel.partialTraj κ a b) u).compProd (ProbabilityTheory.Kernel.traj κ b) = MeasureTheory.Measure.map (fun x => (Preorder.frestrictLe b x, x)) ((ProbabilityTheory.Kernel.traj κ a) u) - ProbabilityTheory.Kernel.condDistrib_trajMeasure 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {μ₀ : MeasureTheory.Measure (X 0)} [MeasureTheory.IsProbabilityMeasure μ₀] {a : ℕ} [StandardBorelSpace (X (a + 1))] [Nonempty (X (a + 1))] : ⇑(ProbabilityTheory.condDistrib (fun x => x (a + 1)) (Preorder.frestrictLe a) (ProbabilityTheory.Kernel.trajMeasure μ₀ κ)) =ᵐ[MeasureTheory.Measure.map (Preorder.frestrictLe a) (ProbabilityTheory.Kernel.trajMeasure μ₀ κ)] ⇑(κ a) - ProbabilityTheory.Kernel.partialTraj_compProd_eq_map_traj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {a b : ℕ} (hab : a ≤ b) {x₀ : (n : ↥(Finset.Iic a)) → X ↑n} : ((ProbabilityTheory.Kernel.partialTraj κ a b) x₀).compProd (κ b) = MeasureTheory.Measure.map (fun x => (Preorder.frestrictLe b x, x (b + 1))) ((ProbabilityTheory.Kernel.traj κ a) x₀) - ProbabilityTheory.Kernel.aestronglyMeasurable_traj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {E : Type u_2} [NormedAddCommGroup E] {a b : ℕ} (hab : a ≤ b) {f : ((n : ℕ) → X n) → E} {x₀ : (i : ↥(Finset.Iic a)) → X ↑i} (hf : MeasureTheory.AEStronglyMeasurable f ((ProbabilityTheory.Kernel.traj κ a) x₀)) : ∀ᵐ (x : (i : ↥(Finset.Iic b)) → X ↑i) ∂(ProbabilityTheory.Kernel.partialTraj κ a b) x₀, MeasureTheory.AEStronglyMeasurable f ((ProbabilityTheory.Kernel.traj κ b) x) - ProbabilityTheory.Kernel.integral_traj_partialTraj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] {a b : ℕ} (hab : a ≤ b) {x₀ : (i : ↥(Finset.Iic a)) → X ↑i} {f : ((n : ℕ) → X n) → E} (hf : MeasureTheory.Integrable f ((ProbabilityTheory.Kernel.traj κ a) x₀)) : ∫ (x : (i : ↥(Finset.Iic b)) → X ↑i), ∫ (y : (n : ℕ) → X n), f y ∂(ProbabilityTheory.Kernel.traj κ b) x ∂(ProbabilityTheory.Kernel.partialTraj κ a b) x₀ = ∫ (x : (n : ℕ) → X n), f x ∂(ProbabilityTheory.Kernel.traj κ a) x₀ - ProbabilityTheory.Kernel.condExp_traj' 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [CompleteSpace E] {a b c : ℕ} (hab : a ≤ b) (hbc : b ≤ c) (x₀ : (i : ↥(Finset.Iic a)) → X ↑i) (f : ((n : ℕ) → X n) → E) : ((ProbabilityTheory.Kernel.traj κ a) x₀)[f | ↑MeasureTheory.Filtration.piLE b] =ᵐ[(ProbabilityTheory.Kernel.traj κ a) x₀] fun x => ∫ (y : (i : ↥(Finset.Iic c)) → X ↑i), ((ProbabilityTheory.Kernel.traj κ a) x₀)[f | ↑MeasureTheory.Filtration.piLE c] (Function.updateFinset x (Finset.Iic c) y) ∂(ProbabilityTheory.Kernel.partialTraj κ b c) (Preorder.frestrictLe b x) - ProbabilityTheory.Kernel.setIntegral_traj_partialTraj 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] {a b : ℕ} (hab : a ≤ b) {x₀ : (i : ↥(Finset.Iic a)) → X ↑i} {f : ((n : ℕ) → X n) → E} (hf : MeasureTheory.Integrable f ((ProbabilityTheory.Kernel.traj κ a) x₀)) {A : Set ((i : ↥(Finset.Iic b)) → X ↑i)} (hA : MeasurableSet A) : ∫ (x : (i : ↥(Finset.Iic b)) → X ↑i) in A, ∫ (y : (n : ℕ) → X n), f y ∂(ProbabilityTheory.Kernel.traj κ b) x ∂(ProbabilityTheory.Kernel.partialTraj κ a b) x₀ = ∫ (y : (n : ℕ) → X n) in Preorder.frestrictLe b ⁻¹' A, f y ∂(ProbabilityTheory.Kernel.traj κ a) x₀ - ProbabilityTheory.Kernel.integral_traj_partialTraj' 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] {a b : ℕ} (hab : a ≤ b) {x₀ : (i : ↥(Finset.Iic a)) → X ↑i} {f : ((i : ↥(Finset.Iic b)) → X ↑i) → ((n : ℕ) → X n) → E} (hf : MeasureTheory.Integrable (Function.uncurry f) (((ProbabilityTheory.Kernel.partialTraj κ a b) x₀).compProd (ProbabilityTheory.Kernel.traj κ b))) : ∫ (x : (i : ↥(Finset.Iic b)) → X ↑i), ∫ (y : (n : ℕ) → X n), f x y ∂(ProbabilityTheory.Kernel.traj κ b) x ∂(ProbabilityTheory.Kernel.partialTraj κ a b) x₀ = ∫ (x : (n : ℕ) → X n), f (Preorder.frestrictLe b x) x ∂(ProbabilityTheory.Kernel.traj κ a) x₀ - ProbabilityTheory.Kernel.traj_eq_prod 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] (a : ℕ) : ProbabilityTheory.Kernel.traj κ a = (ProbabilityTheory.Kernel.id.prod ((ProbabilityTheory.Kernel.traj κ a).map (Set.Ioi a).domRestrict)).map ⇑(MeasurableEquiv.IicProdIoi a) - ProbabilityTheory.Kernel.setIntegral_traj_partialTraj' 📋 Mathlib.Probability.Kernel.IonescuTulcea.Traj
{X : ℕ → Type u_1} [(n : ℕ) → MeasurableSpace (X n)] {κ : (n : ℕ) → ProbabilityTheory.Kernel ((i : ↥(Finset.Iic n)) → X ↑i) (X (n + 1))} [∀ (n : ℕ), ProbabilityTheory.IsMarkovKernel (κ n)] {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] {a b : ℕ} (hab : a ≤ b) {u : (i : ↥(Finset.Iic a)) → X ↑i} {f : ((i : ↥(Finset.Iic b)) → X ↑i) → ((n : ℕ) → X n) → E} (hf : MeasureTheory.Integrable (Function.uncurry f) (((ProbabilityTheory.Kernel.partialTraj κ a b) u).compProd (ProbabilityTheory.Kernel.traj κ b))) {A : Set ((i : ↥(Finset.Iic b)) → X ↑i)} (hA : MeasurableSet A) : ∫ (x : (i : ↥(Finset.Iic b)) → X ↑i) in A, ∫ (y : (n : ℕ) → X n), f x y ∂(ProbabilityTheory.Kernel.traj κ b) x ∂(ProbabilityTheory.Kernel.partialTraj κ a b) u = ∫ (y : (n : ℕ) → X n) in Preorder.frestrictLe b ⁻¹' A, f (Preorder.frestrictLe b y) y ∂(ProbabilityTheory.Kernel.traj κ a) u - ProbabilityTheory.Kernel.IsDeterministic.exists_eq_deterministic 📋 Mathlib.Probability.Kernel.Deterministic
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} [StandardBorelSpace β] (κ : ProbabilityTheory.Kernel α β) [ProbabilityTheory.IsMarkovKernel κ] [ProbabilityTheory.IsDeterministic κ] : ∃ f, ∃ (hf : Measurable f), κ = ProbabilityTheory.Kernel.deterministic f hf - ProbabilityTheory.Kernel.comp_parallelComp_comp_copy 📋 Mathlib.Probability.Kernel.Deterministic
{α : Type u_1} {β : Type u_2} {mα : MeasurableSpace α} {mβ : MeasurableSpace β} {γ : Type u_3} [MeasurableSpace γ] {κ : ProbabilityTheory.Kernel α β} {η : ProbabilityTheory.Kernel β γ} [ProbabilityTheory.IsMarkovKernel κ] [ProbabilityTheory.IsMarkovKernel η] [ProbabilityTheory.IsDeterministic (η.comp κ)] : ((η.comp κ).parallelComp κ).comp (ProbabilityTheory.Kernel.copy α) = ((η.parallelComp ProbabilityTheory.Kernel.id).comp (ProbabilityTheory.Kernel.copy β)).comp κ - ProbabilityTheory.instIsMarkovKernelPosterior 📋 Mathlib.Probability.Kernel.Posterior
{Ω : Type u_1} {𝓧 : Type u_2} {mΩ : MeasurableSpace Ω} {m𝓧 : MeasurableSpace 𝓧} {κ : ProbabilityTheory.Kernel Ω 𝓧} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] [ProbabilityTheory.IsFiniteKernel κ] [StandardBorelSpace Ω] [Nonempty Ω] : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.posterior κ μ) - ProbabilityTheory.posterior_comp_self 📋 Mathlib.Probability.Kernel.Posterior
{Ω : Type u_1} {𝓧 : Type u_2} {mΩ : MeasurableSpace Ω} {m𝓧 : MeasurableSpace 𝓧} {κ : ProbabilityTheory.Kernel Ω 𝓧} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] [ProbabilityTheory.IsFiniteKernel κ] [StandardBorelSpace Ω] [Nonempty Ω] [ProbabilityTheory.IsMarkovKernel κ] : (μ.bind ⇑κ).bind ⇑(ProbabilityTheory.posterior κ μ) = μ - ProbabilityTheory.posterior_posterior 📋 Mathlib.Probability.Kernel.Posterior
{Ω : Type u_1} {𝓧 : Type u_2} {mΩ : MeasurableSpace Ω} {m𝓧 : MeasurableSpace 𝓧} {κ : ProbabilityTheory.Kernel Ω 𝓧} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] [ProbabilityTheory.IsFiniteKernel κ] [StandardBorelSpace Ω] [Nonempty Ω] [StandardBorelSpace 𝓧] [Nonempty 𝓧] [ProbabilityTheory.IsMarkovKernel κ] : ⇑(ProbabilityTheory.posterior (ProbabilityTheory.posterior κ μ) (μ.bind ⇑κ)) =ᵐ[μ] ⇑κ - ProbabilityTheory.bayesRisk_le_avgRisk 📋 Mathlib.Probability.Decision.Risk.Basic
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_4} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} (ℓ : Θ → 𝓨 → ENNReal) (P : ProbabilityTheory.Kernel Θ 𝓧) (κ : ProbabilityTheory.Kernel 𝓧 𝓨) (π : MeasureTheory.Measure Θ) [hκ : ProbabilityTheory.IsMarkovKernel κ] : ProbabilityTheory.bayesRisk ℓ P π ≤ ProbabilityTheory.avgRisk ℓ P κ π - ProbabilityTheory.bayesRisk_le_mul 📋 Mathlib.Probability.Decision.Risk.Basic
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_4} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {ℓ : Θ → 𝓨 → ENNReal} [Nonempty 𝓨] (P : ProbabilityTheory.Kernel Θ 𝓧) [ProbabilityTheory.IsMarkovKernel P] (π : MeasureTheory.Measure Θ) [MeasureTheory.IsProbabilityMeasure π] {C : NNReal} (hℓC : ∀ (θ : Θ) (y : 𝓨), ℓ θ y ≤ ↑C) : ProbabilityTheory.bayesRisk ℓ P π ≤ ↑C - ProbabilityTheory.avgRisk_const_right 📋 Mathlib.Probability.Decision.Risk.Basic
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_4} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} (ℓ : Θ → 𝓨 → ENNReal) (P : ProbabilityTheory.Kernel Θ 𝓧) [ProbabilityTheory.IsMarkovKernel P] (ν : MeasureTheory.Measure 𝓨) (π : MeasureTheory.Measure Θ) : ProbabilityTheory.avgRisk ℓ P (ProbabilityTheory.Kernel.const 𝓧 ν) π = ∫⁻ (θ : Θ), ∫⁻ (y : 𝓨), ℓ θ y ∂ν ∂π - ProbabilityTheory.bayesRisk_le_bayesRisk_comp 📋 Mathlib.Probability.Decision.Risk.Basic
{Θ : Type u_1} {𝓧 : Type u_2} {𝓧' : Type u_3} {𝓨 : Type u_4} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓧' : MeasurableSpace 𝓧'} {m𝓨 : MeasurableSpace 𝓨} (ℓ : Θ → 𝓨 → ENNReal) (P : ProbabilityTheory.Kernel Θ 𝓧) (π : MeasureTheory.Measure Θ) (η : ProbabilityTheory.Kernel 𝓧 𝓧') [ProbabilityTheory.IsMarkovKernel η] : ProbabilityTheory.bayesRisk ℓ P π ≤ ProbabilityTheory.bayesRisk ℓ (η.comp P) π - ProbabilityTheory.avgRisk_le_mul 📋 Mathlib.Probability.Decision.Risk.Basic
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_4} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {ℓ : Θ → 𝓨 → ENNReal} (P : ProbabilityTheory.Kernel Θ 𝓧) [ProbabilityTheory.IsMarkovKernel P] (κ : ProbabilityTheory.Kernel 𝓧 𝓨) [ProbabilityTheory.IsMarkovKernel κ] (π : MeasureTheory.Measure Θ) [MeasureTheory.IsProbabilityMeasure π] {C : NNReal} (hℓC : ∀ (θ : Θ) (y : 𝓨), ℓ θ y ≤ ↑C) : ProbabilityTheory.avgRisk ℓ P κ π ≤ ↑C - ProbabilityTheory.bayesRisk_le_iInf 📋 Mathlib.Probability.Decision.Risk.Basic
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_4} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {ℓ : Θ → 𝓨 → ENNReal} (hl : Measurable (Function.uncurry ℓ)) (P : ProbabilityTheory.Kernel Θ 𝓧) [ProbabilityTheory.IsMarkovKernel P] (π : MeasureTheory.Measure Θ) : ProbabilityTheory.bayesRisk ℓ P π ≤ ⨅ y, ∫⁻ (θ : Θ), ℓ θ y ∂π - ProbabilityTheory.bayesRisk_of_subsingleton 📋 Mathlib.Probability.Decision.Risk.Basic
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_4} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {ℓ : Θ → 𝓨 → ENNReal} {P : ProbabilityTheory.Kernel Θ 𝓧} {π : MeasureTheory.Measure Θ} [Subsingleton 𝓧] [Nonempty 𝓨] [ProbabilityTheory.IsMarkovKernel P] [MeasureTheory.SFinite π] (hl : Measurable (Function.uncurry ℓ)) : ProbabilityTheory.bayesRisk ℓ P π = ⨅ y, ∫⁻ (θ : Θ), ℓ θ y ∂π - ProbabilityTheory.bayesRisk_compProd_le_bayesRisk 📋 Mathlib.Probability.Decision.Risk.Basic
{Θ : Type u_1} {𝓧 : Type u_2} {𝓧' : Type u_3} {𝓨 : Type u_4} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓧' : MeasurableSpace 𝓧'} {m𝓨 : MeasurableSpace 𝓨} (ℓ : Θ → 𝓨 → ENNReal) (P : ProbabilityTheory.Kernel Θ 𝓧) [ProbabilityTheory.IsSFiniteKernel P] (π : MeasureTheory.Measure Θ) (η : ProbabilityTheory.Kernel (Θ × 𝓧) 𝓧') [ProbabilityTheory.IsMarkovKernel η] : ProbabilityTheory.bayesRisk ℓ (P.compProd η) π ≤ ProbabilityTheory.bayesRisk ℓ P π - ProbabilityTheory.lintegral_iInf_posterior_le_avgRisk 📋 Mathlib.Probability.Decision.BayesEstimator
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_3} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {ℓ : Θ → 𝓨 → ENNReal} [StandardBorelSpace Θ] [Nonempty Θ] (hl : Measurable (Function.uncurry ℓ)) (P : ProbabilityTheory.Kernel Θ 𝓧) [ProbabilityTheory.IsFiniteKernel P] (κ : ProbabilityTheory.Kernel 𝓧 𝓨) [ProbabilityTheory.IsMarkovKernel κ] (π : MeasureTheory.Measure Θ) [MeasureTheory.IsFiniteMeasure π] : ∫⁻ (x : 𝓧), ⨅ y, ∫⁻ (θ : Θ), ℓ θ y ∂(ProbabilityTheory.posterior P π) x ∂π.bind ⇑P ≤ ProbabilityTheory.avgRisk ℓ P κ π - ProbabilityTheory.bayesRisk_countable 📋 Mathlib.Probability.Decision.Risk.Countable
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_3} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {ℓ : Θ → 𝓨 → ENNReal} {P : ProbabilityTheory.Kernel Θ 𝓧} {π : MeasureTheory.Measure Θ} [Countable Θ] [MeasurableSingletonClass Θ] : ProbabilityTheory.bayesRisk ℓ P π = ⨅ κ, ⨅ (_ : ProbabilityTheory.IsMarkovKernel κ), ∑' (θ : Θ), (∫⁻ (y : 𝓨), ℓ θ y ∂(κ.comp P) θ) * π {θ} - ProbabilityTheory.bayesRisk_fintype 📋 Mathlib.Probability.Decision.Risk.Countable
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_3} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {ℓ : Θ → 𝓨 → ENNReal} {P : ProbabilityTheory.Kernel Θ 𝓧} {π : MeasureTheory.Measure Θ} [Fintype Θ] [MeasurableSingletonClass Θ] : ProbabilityTheory.bayesRisk ℓ P π = ⨅ κ, ⨅ (_ : ProbabilityTheory.IsMarkovKernel κ), ∑ θ, (∫⁻ (y : 𝓨), ℓ θ y ∂(κ.comp P) θ) * π {θ} - ProbabilityTheory.bayesRisk_countable' 📋 Mathlib.Probability.Decision.Risk.Countable
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_3} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {ℓ : Θ → 𝓨 → ENNReal} {P : ProbabilityTheory.Kernel Θ 𝓧} {π : MeasureTheory.Measure Θ} [Countable 𝓨] [MeasurableSingletonClass 𝓨] (hℓ : Measurable ℓ) : ProbabilityTheory.bayesRisk ℓ P π = ⨅ κ, ⨅ (_ : ProbabilityTheory.IsMarkovKernel κ), ∑' (y : 𝓨), ∫⁻ (θ : Θ), ℓ θ y * ((P θ).bind ⇑κ) {y} ∂π - ProbabilityTheory.bayesRisk_fintype' 📋 Mathlib.Probability.Decision.Risk.Countable
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_3} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {ℓ : Θ → 𝓨 → ENNReal} {P : ProbabilityTheory.Kernel Θ 𝓧} {π : MeasureTheory.Measure Θ} [Fintype 𝓨] [MeasurableSingletonClass 𝓨] (hℓ : Measurable ℓ) : ProbabilityTheory.bayesRisk ℓ P π = ⨅ κ, ⨅ (_ : ProbabilityTheory.IsMarkovKernel κ), ∑ y, ∫⁻ (θ : Θ), ℓ θ y * ((P θ).bind ⇑κ) {y} ∂π - ProbabilityTheory.riskIncrease_comp_le 📋 Mathlib.Probability.Decision.Risk.RiskIncrease
{Θ : Type u_1} {𝓧 : Type u_2} {𝓧' : Type u_3} {𝓨 : Type u_4} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} {m𝓧' : MeasurableSpace 𝓧'} [MeasurableSpace 𝓨] (ℓ : Θ → 𝓨 → ENNReal) (P : ProbabilityTheory.Kernel Θ 𝓧) (π : MeasureTheory.Measure Θ) (η : ProbabilityTheory.Kernel 𝓧 𝓧') [ProbabilityTheory.IsMarkovKernel η] : ProbabilityTheory.riskIncrease ℓ (η.comp P) π ≤ ProbabilityTheory.riskIncrease ℓ P π - ProbabilityTheory.riskIncrease_lt_top 📋 Mathlib.Probability.Decision.Risk.RiskIncrease
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_4} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} [MeasurableSpace 𝓨] {π : MeasureTheory.Measure Θ} {P : ProbabilityTheory.Kernel Θ 𝓧} {ℓ : Θ → 𝓨 → ENNReal} (hℓ : Measurable (Function.uncurry ℓ)) [ProbabilityTheory.IsMarkovKernel P] [MeasureTheory.IsFiniteMeasure π] {y : 𝓨} (h_finite : ∫⁻ (θ : Θ), ℓ θ y ∂π ≠ ⊤) : ProbabilityTheory.riskIncrease ℓ P π < ⊤ - ProbabilityTheory.riskIncrease_le_iInf 📋 Mathlib.Probability.Decision.Risk.RiskIncrease
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_4} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} [MeasurableSpace 𝓨] {π : MeasureTheory.Measure Θ} {P : ProbabilityTheory.Kernel Θ 𝓧} {ℓ : Θ → 𝓨 → ENNReal} (hℓ : Measurable (Function.uncurry ℓ)) [ProbabilityTheory.IsMarkovKernel P] [MeasureTheory.SFinite π] : ProbabilityTheory.riskIncrease ℓ P π ≤ ⨅ z, ∫⁻ (θ : Θ), ℓ θ z ∂π - ProbabilityTheory.riskIncrease_eq_iInf_sub 📋 Mathlib.Probability.Decision.Risk.RiskIncrease
{Θ : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_4} {mΘ : MeasurableSpace Θ} {m𝓧 : MeasurableSpace 𝓧} [MeasurableSpace 𝓨] {ℓ : Θ → 𝓨 → ENNReal} (hℓ : Measurable (Function.uncurry ℓ)) (P : ProbabilityTheory.Kernel Θ 𝓧) [ProbabilityTheory.IsMarkovKernel P] (π : MeasureTheory.Measure Θ) [MeasureTheory.SFinite π] : ProbabilityTheory.riskIncrease ℓ P π = (⨅ z, ∫⁻ (θ : Θ), ℓ θ z ∂π) - ProbabilityTheory.bayesRisk ℓ P π - ProbabilityTheory.HasCondDistrib.of_compProd 📋 Mathlib.Probability.HasCondDistrib
{Ω : Type u_1} {𝓧 : Type u_2} {𝓨 : Type u_3} {𝓩 : Type u_4} {mΩ : MeasurableSpace Ω} {m𝓧 : MeasurableSpace 𝓧} {m𝓨 : MeasurableSpace 𝓨} {m𝓩 : MeasurableSpace 𝓩} {P : MeasureTheory.Measure Ω} {X : Ω → 𝓧} {Y : Ω → 𝓨} {κ : ProbabilityTheory.Kernel 𝓧 𝓨} [MeasureTheory.SFinite P] [ProbabilityTheory.IsSFiniteKernel κ] {Z : Ω → 𝓩} {η : ProbabilityTheory.Kernel (𝓧 × 𝓨) 𝓩} [ProbabilityTheory.IsMarkovKernel η] (h : ProbabilityTheory.HasCondDistrib (fun a => (Y a, Z a)) X (κ.compProd η) P) : ProbabilityTheory.HasCondDistrib Z (fun a => (X a, Y a)) η P - ProbabilityTheory.instIsMarkovKernelCondExpKernel 📋 Mathlib.Probability.Kernel.Condexp
{Ω : Type u_1} {m : MeasurableSpace Ω} [mΩ : MeasurableSpace Ω] [StandardBorelSpace Ω] {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] : ProbabilityTheory.IsMarkovKernel (ProbabilityTheory.condExpKernel μ m) - SFinKer.deterministic_deterministic 📋 Mathlib.Probability.Kernel.Category.SFinKer
(X Y : SFinKer) (κ : ProbabilityTheory.Kernel X.carrier Y.carrier) [ProbabilityTheory.IsDeterministic κ] [ProbabilityTheory.IsMarkovKernel κ] : CategoryTheory.Deterministic { hom := κ, property := ⋯ } - instIsMarkovKernelCarrierObjSFinKerStochHomHomHomWideSubcategory 📋 Mathlib.Probability.Kernel.Category.Stoch
{X Y : Stoch} (κ : X ⟶ Y) : ProbabilityTheory.IsMarkovKernel κ.hom.hom - instDeterministicStochMkWideSubcategorySFinKerStochHomMk 📋 Mathlib.Probability.Kernel.Category.Stoch
(X Y : Stoch) (κ : ProbabilityTheory.Kernel X.obj.carrier Y.obj.carrier) [ProbabilityTheory.IsDeterministic κ] [ProbabilityTheory.IsMarkovKernel κ] : CategoryTheory.Deterministic { hom := { hom := κ, property := ⋯ }, property := ⋯ } - ProbabilityTheory.Kernel.IsReversible.invariant 📋 Mathlib.Probability.Kernel.Invariance
{α : Type u_1} {mα : MeasurableSpace α} {κ : ProbabilityTheory.Kernel α α} [ProbabilityTheory.IsMarkovKernel κ] {π : MeasureTheory.Measure α} (h_rev : κ.IsReversible π) : κ.Invariant π - ProbabilityTheory.Kernel.exists_measurable_map_eq_unitInterval 📋 Mathlib.Probability.Kernel.Representation
{X : Type u_1} {Y : Type u_2} {mX : MeasurableSpace X} [Nonempty Y] {mY : MeasurableSpace Y} [StandardBorelSpace Y] (κ : ProbabilityTheory.Kernel X Y) [ProbabilityTheory.IsMarkovKernel κ] : ∃ f, Measurable (Function.uncurry f) ∧ ∀ (a : X), MeasureTheory.Measure.map (f a) MeasureTheory.volume = κ a
About
Loogle searches Lean and Mathlib definitions and theorems.
You can use Loogle from within the Lean4 VSCode language extension
using the Loogle command from the command palette. You can also try the
#loogle command from LeanSearchClient,
the CLI version, the Loogle
VS Code extension, the lean.nvim
integration or the Zulip bot.
Usage
Loogle finds definitions and lemmas in various ways:
By constant:
🔍Real.sin
finds all lemmas whose statement somehow mentions the sine function.By lemma name substring:
🔍"differ"
finds all lemmas that have"differ"somewhere in their lemma name.By subexpression:
🔍_ * (_ ^ _)
finds all lemmas whose statements somewhere include a product where the second argument is raised to some power.The pattern can also be non-linear, as in
🔍Real.sqrt ?a * Real.sqrt ?aIf the pattern has parameters, they are matched in any order. Both of these will find
List.map:
🔍(?a -> ?b) -> List ?a -> List ?b
🔍List ?a -> (?a -> ?b) -> List ?bBy main conclusion:
🔍|- tsum _ = _ * tsum _
finds all lemmas where the conclusion (the subexpression to the right of all→and∀) has the given shape.As before, if the pattern has parameters, they are matched against the hypotheses of the lemma in any order; for example,
🔍|- _ < _ → tsum _ < tsum _
will findtsum_lt_tsumeven though the hypothesisf i < g iis not the last.You can filter for definitions vs theorems: Using
⊢ (_ : Type _)finds all definitions which provide data while⊢ (_ : Prop)finds all theorems (and definitions of proofs).
If you pass more than one such search filter, separated by commas
Loogle will return lemmas which match all of them. The
search
🔍 Real.sin, "two", tsum, _ * _, _ ^ _, |- _ < _ → _
would find all lemmas which mention the constants Real.sin
and tsum, have "two" as a substring of the
lemma name, include a product and a power somewhere in the type,
and have a hypothesis of the form _ < _ (if
there were any such lemmas). Metavariables (?a) are
assigned independently in each filter.
The #lucky button will directly send you to the
documentation of the first hit.
Source code
You can find the source code for this service at https://github.com/nomeata/loogle. The https://loogle.lean-lang.org/ service is provided by the Lean FRO. Please review the Lean FRO Terms of Use and Privacy Policy.
This is Loogle revision 9f11169 serving mathlib revision ce5dd8c