mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.Probability.Kernel.Disintegration.CDFToKernel

Goal state before:
pp: case iUnion
α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
κ : Kernel α (β × ℝ)
ν : Kernel α β
f : α × β → StieltjesFunction ℝ
inst✝ : IsFiniteKernel κ
hf : IsCondKernelCDF f κ ν
a : α
s : Set (β × ℝ)
f' : ℕ → Set (β × ℝ)
hf_disj : Pairwise (Disjoint on f')
hf_meas : ∀ (i : ℕ), MeasurableSet (f' i)
hf_eq : ∀ (i : ℕ), ∫⁻ (b : β), ((IsCondKernelCDF.toKernel f hf) (a, b)) (Prod.mk b ⁻¹' f' i) ∂ν a = (κ a) (f' i)
h_eq : ∀ (a : β), Prod.mk a ⁻¹' ⋃ i, f' i = ⋃ i, Prod.mk a ⁻¹' f' i
⊢ ∫⁻ (b : β), ((IsCondKernelCDF.toKernel f hf) (a, b)) (⋃ i, Prod.mk b ⁻¹' f' i) ∂ν a = (κ a) (⋃ i, f' i)

Tactic:
have h_disj : ∀ a, Pairwise (Disjoint on fun i ↦ Prod.mk a ⁻¹' f' i) := by
      intro _ _ _ hij
      exact Disjoint.preimage _ (hf_disj hij)

Goal state after:
case iUnion
α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
κ : Kernel α (β × ℝ)
ν : Kernel α β
f : α × β → StieltjesFunction ℝ
inst✝ : IsFiniteKernel κ
hf : IsCondKernelCDF f κ ν
a : α
s : Set (β × ℝ)
f' : ℕ → Set (β × ℝ)
hf_disj : Pairwise (Disjoint on f')
hf_meas : ∀ (i : ℕ), MeasurableSet (f' i)
hf_eq : ∀ (i : ℕ), ∫⁻ (b : β), ((IsCondKernelCDF.toKernel f hf) (a, b)) (Prod.mk b ⁻¹' f' i) ∂ν a = (κ a) (f' i)
h_eq : ∀ (a : β), Prod.mk a ⁻¹' ⋃ i, f' i = ⋃ i, Prod.mk a ⁻¹' f' i
h_disj : ∀ (a : β), Pairwise (Disjoint on fun i ↦ Prod.mk a ⁻¹' f' i)
⊢ ∫⁻ (b : β), ((IsCondKernelCDF.toKernel f hf) (a, b)) (⋃ i, Prod.mk b ⁻¹' f' i) ∂ν a = (κ a) (⋃ i, f' i)
Module: Mathlib.Probability.Kernel.Disintegration.StandardBorel

Goal state before:
pp: α : Type u_1
β : Type u_2
γ : Type u_3
Ω : Type u_4
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝³ : CountablyGenerated γ
mΩ : MeasurableSpace Ω
inst✝² : StandardBorelSpace Ω
inst✝¹ : Nonempty Ω
κ : Kernel Unit (α × ℝ)
inst✝ : IsFiniteKernel κ
⊢ κ.fst ⊗ₖ κ.condKernelUnitReal = κ

Tactic:
condKernelUnitReal,

Goal state after:
α : Type u_1
β : Type u_2
γ : Type u_3
Ω : Type u_4
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝³ : CountablyGenerated γ
mΩ : MeasurableSpace Ω
inst✝² : StandardBorelSpace Ω
inst✝¹ : Nonempty Ω
κ : Kernel Unit (α × ℝ)
inst✝ : IsFiniteKernel κ
⊢ κ.fst ⊗ₖ IsCondKernelCDF.toKernel (fun p ↦ condCDF (κ ()) p.2) ⋯ = κ
Module: Mathlib.Probability.Kernel.Disintegration.StandardBorel

Goal state before:
pp: α : Type u_1
β : Type u_2
γ : Type u_3
Ω : Type u_4
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝³ : CountablyGenerated γ
mΩ : MeasurableSpace Ω
inst✝² : StandardBorelSpace Ω
inst✝¹ : Nonempty Ω
η : Kernel α ℝ
inst✝ : IsMarkovKernel η
a : α
⊢ ((piecewise ⋯ η (deterministic (fun x ↦ Exists.choose ⋯) ⋯)) a) (range (embeddingReal Ω)) = 1

Tactic:
piecewise_apply

Goal state after:
α : Type u_1
β : Type u_2
γ : Type u_3
Ω : Type u_4
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝³ : CountablyGenerated γ
mΩ : MeasurableSpace Ω
inst✝² : StandardBorelSpace Ω
inst✝¹ : Nonempty Ω
η : Kernel α ℝ
inst✝ : IsMarkovKernel η
a : α
⊢ (if a ∈ (fun a ↦ (η a) (range (embeddingReal Ω))ᶜ) ⁻¹' {0} then η a
      else (deterministic (fun x ↦ Exists.choose ⋯) ⋯) a)
      (range (embeddingReal Ω)) =
    1
Module: Mathlib.Probability.Kernel.Disintegration.StandardBorel

Goal state before:
pp: α : Type u_1
β : Type u_2
Ω : Type u_4
mα : MeasurableSpace α
mβ : MeasurableSpace β
mΩ : MeasurableSpace Ω
inst✝³ : StandardBorelSpace Ω
inst✝² : Nonempty Ω
κ : Kernel α (β × Ω)
inst✝¹ : IsSFiniteKernel κ
η : Kernel (α × β) ℝ
inst✝ : IsSFiniteKernel η
hη : (κ.map (Prod.map id (embeddingReal Ω))).fst ⊗ₖ η = κ.map (Prod.map id (embeddingReal Ω))
e : Ω → ℝ := embeddingReal Ω
he : MeasurableEmbedding (embeddingReal Ω) := measurableEmbedding_embeddingReal Ω
κ' : Kernel α (β × ℝ) := κ.map (Prod.map id e)
hη' : κ'.fst ⊗ₖ η = κ'
⊢ κ.fst ⊗ₖ borelMarkovFromReal Ω η = ((κ.map (Prod.map id (embeddingReal Ω))).fst ⊗ₖ η).comapRight ⋯

Tactic:
have h_prod_embed : MeasurableEmbedding (Prod.map (id : β → β) e) :=
    MeasurableEmbedding.id.prodMap he

Goal state after:
α : Type u_1
β : Type u_2
Ω : Type u_4
mα : MeasurableSpace α
mβ : MeasurableSpace β
mΩ : MeasurableSpace Ω
inst✝³ : StandardBorelSpace Ω
inst✝² : Nonempty Ω
κ : Kernel α (β × Ω)
inst✝¹ : IsSFiniteKernel κ
η : Kernel (α × β) ℝ
inst✝ : IsSFiniteKernel η
hη : (κ.map (Prod.map id (embeddingReal Ω))).fst ⊗ₖ η = κ.map (Prod.map id (embeddingReal Ω))
e : Ω → ℝ := embeddingReal Ω
he : MeasurableEmbedding (embeddingReal Ω) := measurableEmbedding_embeddingReal Ω
κ' : Kernel α (β × ℝ) := κ.map (Prod.map id e)
hη' : κ'.fst ⊗ₖ η = κ'
h_prod_embed : MeasurableEmbedding (Prod.map id e)
⊢ κ.fst ⊗ₖ borelMarkovFromReal Ω η = ((κ.map (Prod.map id (embeddingReal Ω))).fst ⊗ₖ η).comapRight ⋯
Module: Mathlib.Probability.Kernel.Disintegration.StandardBorel

Goal state before:
pp: α : Type u_1
β : Type u_2
Ω : Type u_4
mα : MeasurableSpace α
mβ : MeasurableSpace β
mΩ : MeasurableSpace Ω
inst✝³ : StandardBorelSpace Ω
inst✝² : Nonempty Ω
κ : Kernel α (β × Ω)
inst✝¹ : IsSFiniteKernel κ
η : Kernel (α × β) ℝ
inst✝ : IsSFiniteKernel η
hη : (κ.map (Prod.map id (embeddingReal Ω))).fst ⊗ₖ η = κ.map (Prod.map id (embeddingReal Ω))
e : Ω → ℝ := embeddingReal Ω
he : MeasurableEmbedding (embeddingReal Ω) := measurableEmbedding_embeddingReal Ω
κ' : Kernel α (β × ℝ) := κ.map (Prod.map id e)
hη' : κ'.fst ⊗ₖ η = κ'
⊢ κ.fst ⊗ₖ borelMarkovFromReal Ω η = κ

Tactic:
have h_prod_embed : MeasurableEmbedding (Prod.map (id : β → β) e) :=
    MeasurableEmbedding.id.prodMap he

Goal state after:
α : Type u_1
β : Type u_2
Ω : Type u_4
mα : MeasurableSpace α
mβ : MeasurableSpace β
mΩ : MeasurableSpace Ω
inst✝³ : StandardBorelSpace Ω
inst✝² : Nonempty Ω
κ : Kernel α (β × Ω)
inst✝¹ : IsSFiniteKernel κ
η : Kernel (α × β) ℝ
inst✝ : IsSFiniteKernel η
hη : (κ.map (Prod.map id (embeddingReal Ω))).fst ⊗ₖ η = κ.map (Prod.map id (embeddingReal Ω))
e : Ω → ℝ := embeddingReal Ω
he : MeasurableEmbedding (embeddingReal Ω) := measurableEmbedding_embeddingReal Ω
κ' : Kernel α (β × ℝ) := κ.map (Prod.map id e)
hη' : κ'.fst ⊗ₖ η = κ'
h_prod_embed : MeasurableEmbedding (Prod.map id e)
⊢ κ.fst ⊗ₖ borelMarkovFromReal Ω η = κ
Module: Mathlib.Probability.Kernel.Disintegration.Unique

Goal state before:
pp: α : Type u_1
β : Type u_2
Ω : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
inst✝⁵ : MeasurableSpace Ω
inst✝⁴ : StandardBorelSpace Ω
inst✝³ : Nonempty Ω
inst✝² : CountableOrCountablyGenerated α β
ρ : Kernel α (β × Ω)
inst✝¹ : IsFiniteKernel ρ
κ : Kernel (α × β) Ω
inst✝ : IsFiniteKernel κ
hκ : ρ.fst ⊗ₖ κ = ρ
a : α
⊢ ∀ᵐ (x : β) ∂ρ.fst a, κ (a, x) = ρ.condKernel (a, x)

Tactic:
by
  filter_upwards [Kernel.condKernel_apply_eq_condKernel ρ a,
    Kernel.apply_eq_measure_condKernel_of_compProd_eq hκ a] with a h1 h2
  rw [h1, h2]

Goal state after:
Module: Mathlib.Probability.Kernel.Integral

Goal state before:
pp: α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
κ : Kernel α β
E : Type u_3
inst✝² : NormedAddCommGroup E
inst✝¹ : NormedSpace ℝ E
η : Kernel α β
s : Set α
hs : MeasurableSet s
inst✝ : DecidablePred fun x ↦ x ∈ s
a : α
g : β → E
⊢ ∫ (b : β), g b ∂(piecewise hs κ η) a = if a ∈ s then ∫ (b : β), g b ∂κ a else ∫ (b : β), g b ∂η a

Tactic:
piecewise_apply

Goal state after:
α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
κ : Kernel α β
E : Type u_3
inst✝² : NormedAddCommGroup E
inst✝¹ : NormedSpace ℝ E
η : Kernel α β
s : Set α
hs : MeasurableSet s
inst✝ : DecidablePred fun x ↦ x ∈ s
a : α
g : β → E
⊢ (∫ (b : β), g b ∂if a ∈ s then κ a else η a) = if a ∈ s then ∫ (b : β), g b ∂κ a else ∫ (b : β), g b ∂η a
Module: Mathlib.Probability.Kernel.Integral

Goal state before:
pp: α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
κ : Kernel α β
E : Type u_3
inst✝² : NormedAddCommGroup E
inst✝¹ : NormedSpace ℝ E
η : Kernel α β
s : Set α
hs : MeasurableSet s
inst✝ : DecidablePred fun x ↦ x ∈ s
a : α
g : β → E
t : Set β
⊢ ∫ (b : β) in t, g b ∂(piecewise hs κ η) a = if a ∈ s then ∫ (b : β) in t, g b ∂κ a else ∫ (b : β) in t, g b ∂η a

Tactic:
piecewise_apply

Goal state after:
α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
κ : Kernel α β
E : Type u_3
inst✝² : NormedAddCommGroup E
inst✝¹ : NormedSpace ℝ E
η : Kernel α β
s : Set α
hs : MeasurableSet s
inst✝ : DecidablePred fun x ↦ x ∈ s
a : α
g : β → E
t : Set β
⊢ (∫ (b : β) in t, g b ∂if a ∈ s then κ a else η a) =
    if a ∈ s then ∫ (b : β) in t, g b ∂κ a else ∫ (b : β) in t, g b ∂η a
Module: Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj

Goal state before:
pp: case inr.base
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsSFiniteKernel (κ n)
a b : ℕ
x : (i : ↥(Iic a)) → X ↑i
⊢ (partialTraj κ a a) x = ((Kernel.id ×ₖ (partialTraj κ a a).map (restrict₂ ⋯)).map (_root_.IicProdIoc a a)) x

Tactic:
rw [partialTraj_self, id_map, map_apply, prod_apply, IicProdIoc_self, ← Measure.fst,
    Measure.fst_prod]

Goal state after:
case inr.base.hf
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsSFiniteKernel (κ n)
a b : ℕ
x : (i : ↥(Iic a)) → X ↑i
⊢ Measurable (_root_.IicProdIoc a a)

case inr.base
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsSFiniteKernel (κ n)
a b : ℕ
x : (i : ↥(Iic a)) → X ↑i
⊢ Measurable (restrict₂ ⋯)
Module: Mathlib.Probability.Kernel.IonescuTulcea.Traj

Goal state before:
pp: X : ℕ → Type u_1
inst✝ : (n : ℕ) → MeasurableSpace (X n)
μ : (n : ℕ) → Measure ((i : ↥(Iic n)) → X ↑i)
h : ∀ (a b : ℕ) (hab : a ≤ b), Measure.map (frestrictLe₂ hab) (μ b) = μ a
I J : Finset ℕ
hJI : J ⊆ I
sls : J.sup id ≤ I.sup id
⊢ Measure.map (restrict₂ ⋯) (μ (J.sup id)) = Measure.map (restrict₂ ⋯) (μ (I.sup id))

pp: case hg
X : ℕ → Type u_1
inst✝ : (n : ℕ) → MeasurableSpace (X n)
μ : (n : ℕ) → Measure ((i : ↥(Iic n)) → X ↑i)
h : ∀ (a b : ℕ) (hab : a ≤ b), Measure.map (frestrictLe₂ hab) (μ b) = μ a
I J : Finset ℕ
hJI : J ⊆ I
sls : J.sup id ≤ I.sup id
⊢ Measurable (restrict₂ hJI)

pp: case hf
X : ℕ → Type u_1
inst✝ : (n : ℕ) → MeasurableSpace (X n)
μ : (n : ℕ) → Measure ((i : ↥(Iic n)) → X ↑i)
h : ∀ (a b : ℕ) (hab : a ≤ b), Measure.map (frestrictLe₂ hab) (μ b) = μ a
I J : Finset ℕ
hJI : J ⊆ I
sls : J.sup id ≤ I.sup id
⊢ Measurable (restrict₂ ⋯)

Tactic:
← restrict₂_comp_restrict₂ J.subset_Iic_sup_id (Iic_subset_Iic.2 sls),

Goal state after:
X : ℕ → Type u_1
inst✝ : (n : ℕ) → MeasurableSpace (X n)
μ : (n : ℕ) → Measure ((i : ↥(Iic n)) → X ↑i)
h : ∀ (a b : ℕ) (hab : a ≤ b), Measure.map (frestrictLe₂ hab) (μ b) = μ a
I J : Finset ℕ
hJI : J ⊆ I
sls : J.sup id ≤ I.sup id
⊢ Measure.map (restrict₂ ⋯) (μ (J.sup id)) = Measure.map (restrict₂ ⋯ ∘ restrict₂ ⋯) (μ (I.sup id))

case hg
X : ℕ → Type u_1
inst✝ : (n : ℕ) → MeasurableSpace (X n)
μ : (n : ℕ) → Measure ((i : ↥(Iic n)) → X ↑i)
h : ∀ (a b : ℕ) (hab : a ≤ b), Measure.map (frestrictLe₂ hab) (μ b) = μ a
I J : Finset ℕ
hJI : J ⊆ I
sls : J.sup id ≤ I.sup id
⊢ Measurable (restrict₂ hJI)

case hf
X : ℕ → Type u_1
inst✝ : (n : ℕ) → MeasurableSpace (X n)
μ : (n : ℕ) → Measure ((i : ↥(Iic n)) → X ↑i)
h : ∀ (a b : ℕ) (hab : a ≤ b), Measure.map (frestrictLe₂ hab) (μ b) = μ a
I J : Finset ℕ
hJI : J ⊆ I
sls : J.sup id ≤ I.sup id
⊢ Measurable (restrict₂ ⋯)
Module: Mathlib.Probability.Kernel.IonescuTulcea.PartialTraj

Goal state before:
pp: X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsSFiniteKernel (κ n)
a b : ℕ
⊢ partialTraj κ a b = (Kernel.id ×ₖ (partialTraj κ a b).map (restrict₂ ⋯)).map (_root_.IicProdIoc a b)

Tactic:
obtain hba | hab := le_total b a
  · rw [partialTraj_le hba, IicProdIoc_le hba, map_comp_right, ← fst_eq, deterministic_map,
      fst_prod, id_map]
    all_goals fun_prop
  induction b, hab using Nat.le_induction with
  | base =>
    ext1 x
    rw [partialTraj_self, id_map, map_apply, prod_apply, IicProdIoc_self, ← Measure.fst,
    Measure.fst_prod]
    all_goals fun_prop
  | succ k h hk =>
    have : (IicProdIoc (X := X) k (k + 1)) ∘ (Prod.map (IicProdIoc a k) id) =
        (IicProdIoc (h.trans k.le_succ) ∘ (Prod.map id (IocProdIoc a k (k + 1)))) ∘
        prodAssoc := by
      ext x i
      simp only [IicProdIoc_def, MeasurableEquiv.IicProdIoc, MeasurableEquiv.coe_mk,
        Equiv.coe_fn_mk, Function.comp_apply, Prod.map_fst, Prod.map_snd, id_eq,
        Nat.succ_eq_add_one, IocProdIoc]
      split_ifs <;> try rfl
      lia
    nth_rw 1 [← partialTraj_comp_partialTraj h k.le_succ, hk, partialTraj_succ_self, comp_map,
      comap_map_comm, comap_prod, id_comap, ← id_map, map_prod_eq, ← map_comp_right, this,
      map_comp_right, id_prod_eq, prodAssoc_prod, map_comp_right, ← map_prod_map, map_id,
      ← map_comp, map_apply_eq_iff_map_symm_apply_eq, fst_prod_comp_id_prod, ← map_comp_right,
      ← coe_IicProdIoc (h.trans k.le_succ), symm_comp_self, map_id,
      deterministic_congr IicProdIoc_comp_restrict₂.symm, ← deterministic_comp_deterministic,
      comp_deterministic_eq_comap, ← comap_prod, ← map_comp, ← comp_map, ← hk,
      ← partialTraj_comp_partialTraj h k.le_succ, partialTraj_succ_self, map_comp, map_comp,
      ← map_comp_right, ← id_map, map_prod_eq, ← map_comp_right]
    · rfl
    all_goals fun_prop

Goal state after:
Module: Mathlib.Probability.Kernel.RadonNikodym

Goal state before:
pp: α : Type u_1
γ : Type u_2
mα : MeasurableSpace α
mγ : MeasurableSpace γ
κ : Kernel α γ
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
inst✝¹ : IsFiniteKernel κ
f : α → γ → ℝ≥0∞
inst✝ : IsFiniteKernel (κ.withDensity f)
hf : Measurable (Function.uncurry f)
a : α
h_ae : (κ.withDensity f).rnDeriv κ a =ᵐ[κ a] ∂(κ.withDensity f) a/∂κ a
hf' : ∀ (a : α), Measurable (f a)
x : γ
hx1 : (κ.withDensity f).rnDeriv κ a x = (∂(κ.withDensity f) a/∂κ a) x
⊢ (∂(κ a).withDensity (f a)/∂κ a) x = f a x → (κ.withDensity f).rnDeriv κ a x = f a x

Tactic:
hx2

Goal state after:
α : Type u_1
γ : Type u_2
mα : MeasurableSpace α
mγ : MeasurableSpace γ
κ : Kernel α γ
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
inst✝¹ : IsFiniteKernel κ
f : α → γ → ℝ≥0∞
inst✝ : IsFiniteKernel (κ.withDensity f)
hf : Measurable (Function.uncurry f)
a : α
h_ae : (κ.withDensity f).rnDeriv κ a =ᵐ[κ a] ∂(κ.withDensity f) a/∂κ a
hf' : ∀ (a : α), Measurable (f a)
x : γ
hx1 : (κ.withDensity f).rnDeriv κ a x = (∂(κ.withDensity f) a/∂κ a) x
hx2 : (∂(κ a).withDensity (f a)/∂κ a) x = f a x
⊢ (κ.withDensity f).rnDeriv κ a x = f a x
Module: Mathlib.Probability.Kernel.RadonNikodym

Goal state before:
pp: α : Type u_1
γ : Type u_2
mα : MeasurableSpace α
mγ : MeasurableSpace γ
κ : Kernel α γ
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
inst✝¹ : IsFiniteKernel κ
f : α → γ → ℝ≥0∞
inst✝ : IsFiniteKernel (κ.withDensity f)
hf : Measurable (Function.uncurry f)
a : α
h_ae : (κ.withDensity f).rnDeriv κ a =ᵐ[κ a] ∂(κ.withDensity f) a/∂κ a
hf' : ∀ (a : α), Measurable (f a)
x : γ
hx1 : (κ.withDensity f).rnDeriv κ a x = (∂(κ.withDensity f) a/∂κ a) x
hx2 : (∂(κ a).withDensity (f a)/∂κ a) x = f a x
⊢ (∂(κ a).withDensity (f a)/∂κ a) x = f a x

Tactic:
hx2

Goal state after:
α : Type u_1
γ : Type u_2
mα : MeasurableSpace α
mγ : MeasurableSpace γ
κ : Kernel α γ
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
inst✝¹ : IsFiniteKernel κ
f : α → γ → ℝ≥0∞
inst✝ : IsFiniteKernel (κ.withDensity f)
hf : Measurable (Function.uncurry f)
a : α
h_ae : (κ.withDensity f).rnDeriv κ a =ᵐ[κ a] ∂(κ.withDensity f) a/∂κ a
hf' : ∀ (a : α), Measurable (f a)
x : γ
hx1 : (κ.withDensity f).rnDeriv κ a x = (∂(κ.withDensity f) a/∂κ a) x
hx2 : (∂(κ a).withDensity (f a)/∂κ a) x = f a x
⊢ f a x = f a x
Module: Mathlib.Probability.Kernel.CompProdEqIff

Goal state before:
pp: α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
μ : Measure α
κ η : Kernel α β
inst✝³ : MeasurableSpace.CountableOrCountablyGenerated α β
inst✝² : IsFiniteMeasure μ
inst✝¹ : IsFiniteKernel κ
inst✝ : IsFiniteKernel η
h : μ ⊗ₘ κ = μ ⊗ₘ η
h_ac : ∀ᵐ (a : α) ∂μ, κ a ≪ η a
hκ_eq : ∀ᵐ (a : α) ∂μ, κ a = (η.withDensity (κ.rnDeriv η)) a
s : Set (α × β)
hs : MeasurableSet s
x✝ : (μ ⊗ₘ η) s < ∞
⊢ ∫⁻ (x : α × β) in s, κ.rnDeriv η x.1 x.2 ∂μ ⊗ₘ η = (μ ⊗ₘ η) s

Tactic:
calc ∫⁻ x in s, κ.rnDeriv η x.1 x.2 ∂μ ⊗ₘ η
  _ = (μ ⊗ₘ κ) s := by
    rw [Measure.compProd_congr hκ_eq, Measure.compProd_withDensity, withDensity_apply _ hs]
    fun_prop
  _ = (μ ⊗ₘ η) s := by rw [h]

Goal state after:
Module: Mathlib.Probability.Kernel.IonescuTulcea.Traj

Goal state before:
pp: X : ℕ → Type u_1
inst✝¹ : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hb : b ≥ a + 1
⊢ (Kernel.id ×ₖ (partialTraj κ a b).map (restrict₂ ⋯)).map (_root_.IicProdIoc a b) = partialTraj κ a b

pp: case hf
X : ℕ → Type u_1
inst✝¹ : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hb : b ≥ a + 1
⊢ Measurable (frestrictLe b)

pp: case hg
X : ℕ → Type u_1
inst✝¹ : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hb : b ≥ a + 1
⊢ Measurable (restrict₂ ⋯)

pp: X : ℕ → Type u_1
inst✝¹ : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hb : b ≥ a + 1
⊢ MeasurableSpace ((i : ↥(Ioc a b)) → X ↑i)

Tactic:
← partialTraj_eq_prod

Goal state after:
X : ℕ → Type u_1
inst✝¹ : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hb : b ≥ a + 1
⊢ partialTraj κ a b = partialTraj κ a b

case hf
X : ℕ → Type u_1
inst✝¹ : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hb : b ≥ a + 1
⊢ Measurable (frestrictLe b)

case hg
X : ℕ → Type u_1
inst✝¹ : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a b : ℕ
hb : b ≥ a + 1
⊢ Measurable (restrict₂ ⋯)
Module: Mathlib.Probability.ProductMeasure

Goal state before:
pp: ι : Type u_1
X : ι → Type u_2
mX : (i : ι) → MeasurableSpace (X i)
μ : (i : ι) → Measure (X i)
hμ : ∀ (i : ι), IsProbabilityMeasure (μ i)
inst✝² : DecidableEq ι
E : Type u_3
inst✝¹ : NormedAddCommGroup E
inst✝ : NormedSpace ℝ E
s : Finset ι
f : ((i : ι) → X i) → E
mf : StronglyMeasurable f
x : (i : ι) → X i
g : ((i : ↥s) → X ↑i) → E := fun y ↦ f (Function.updateFinset x s y)
this : ∀ (y : (i : ι) → X i), g (s.restrict y) = f y
⊢ ∫ (y : (i : ι) → X i), f y ∂infinitePi μ =
    ∫ (y : (i : ↥s) → X ↑i), f (Function.updateFinset x s y) ∂Measure.pi fun i ↦ μ ↑i

Tactic:
rw [← integral_congr_ae <| ae_of_all _ this, integral_restrict_infinitePi]

Goal state after:
case hf
ι : Type u_1
X : ι → Type u_2
mX : (i : ι) → MeasurableSpace (X i)
μ : (i : ι) → Measure (X i)
hμ : ∀ (i : ι), IsProbabilityMeasure (μ i)
inst✝² : DecidableEq ι
E : Type u_3
inst✝¹ : NormedAddCommGroup E
inst✝ : NormedSpace ℝ E
s : Finset ι
f : ((i : ι) → X i) → E
mf : StronglyMeasurable f
x : (i : ι) → X i
g : ((i : ↥s) → X ↑i) → E := fun y ↦ f (Function.updateFinset x s y)
this : ∀ (y : (i : ι) → X i), g (s.restrict y) = f y
⊢ AEStronglyMeasurable g (Measure.pi fun i ↦ μ ↑i)
Module: Mathlib.Probability.Decision.BayesEstimator

Goal state before:
pp: case e_f
Θ : Type u_1
𝓧 : Type u_2
𝓨 : Type u_3
mΘ : MeasurableSpace Θ
m𝓧 : MeasurableSpace 𝓧
m𝓨 : MeasurableSpace 𝓨
ℓ : Θ → 𝓨 → ℝ≥0∞
inst✝⁴ : StandardBorelSpace Θ
inst✝³ : Nonempty Θ
hl : Measurable (Function.uncurry ℓ)
P : Kernel Θ 𝓧
inst✝² : IsFiniteKernel P
κ : Kernel 𝓧 𝓨
inst✝¹ : IsSFiniteKernel κ
π : Measure Θ
inst✝ : IsFiniteMeasure π
x : 𝓧
⊢ ∫⁻ (x : Θ × 𝓨), ℓ x.1 x.2 ∂(P†π ×ₖ κ) x = ∫⁻ (y : 𝓨), ∫⁻ (θ : Θ), ℓ θ y ∂(P†π) x ∂κ x

Tactic:
rw [Kernel.prod_apply, lintegral_prod_symm' _ (by fun_prop)]

Goal state after:
Module: Mathlib.Probability.Distributions.Geometric

Goal state before:
pp: case pos
p : ↑unitInterval
h : p ≠ 0
⊢ IsProbabilityMeasure (Measure.sum fun n ↦ ENNReal.ofReal ((1 - ↑p) ^ n * ↑p) • Measure.dirac n)

Tactic:
exact (hasSum_one_geometricMeasure h).isProbabilityMeasure_sum_dirac
      (geometricMeasure_nonneg p)

Goal state after:
Module: Mathlib.Probability.Kernel.IonescuTulcea.Traj

Goal state before:
pp: X : ℕ → Type u_1
inst✝⁴ : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝³ : ∀ (n : ℕ), IsMarkovKernel (κ n)
μ₀ : Measure (X 0)
inst✝² : IsProbabilityMeasure μ₀
a : ℕ
inst✝¹ : StandardBorelSpace (X (a + 1))
inst✝ : Nonempty (X (a + 1))
⊢ Measure.map (fun x ↦ (frestrictLe a x, x (a + 1))) (trajMeasure μ₀ κ) =
    Measure.map (frestrictLe a) (trajMeasure μ₀ κ) ⊗ₘ κ a

Tactic:
exact map_frestrictLe_trajMeasure_compProd_eq_map_trajMeasure.symm

Goal state after:
Module: Mathlib.Probability.ProbabilityMassFunction.Constructions

Goal state before:
pp: α : Type u_1
β : Type u_2
f : α → β
mα : MeasurableSpace α
mβ : MeasurableSpace β
p : PMF α
hf : Measurable f
⊢ Measure.map f p.toMeasure = (map f p).toMeasure

Tactic:
by
  ext s hs : 1; rw [PMF.toMeasure_map_apply _ _ _ hf hs, Measure.map_apply hf hs]

Goal state after:
Module: Mathlib.Probability.ProbabilityMassFunction.Constructions

Goal state before:
pp: α : Type u_1
p : PMF α
s : Set α
h : ∃ a ∈ s, a ∈ p.support
a : α
⊢ ¬(p.filter s h) a = 0 ↔ a ∈ s ∧ a ∈ p.support

Tactic:
filter_apply_eq_zero_iff,

Goal state after:
α : Type u_1
p : PMF α
s : Set α
h : ∃ a ∈ s, a ∈ p.support
a : α
⊢ ¬(a ∉ s ∨ a ∉ p.support) ↔ a ∈ s ∧ a ∈ p.support
Module: Mathlib.Probability.Kernel.Category.SFinKer

Goal state before:
pp: case hf
X₁✝ X₂✝ X₃✝ Y₁✝ Y₂✝ Y₃✝ : SFinKer
κ₁ : X₁✝ ⟶ Y₁✝
κ₂ : X₂✝ ⟶ Y₂✝
η : X₃✝ ⟶ Y₃✝
a✝ : (X₁✝.carrier × X₂✝.carrier) × X₃✝.carrier
s✝ : Set (Y₁✝.carrier × Y₂✝.carrier × Y₃✝.carrier)
hs : MeasurableSet s✝
⊢ AEMeasurable (fun x ↦ (η.hom a✝.2) (Prod.mk x ⁻¹' ⇑prodAssoc ⁻¹' s✝)) ((κ₁.hom a✝.1.1).prod (κ₂.hom a✝.1.2))

Tactic:
· refine Measurable.aemeasurable ?_
      exact measurable_measure_prodMk_left (by measurability)

Goal state after:
Module: Mathlib.Probability.Kernel.Category.SFinKer

Goal state before:
pp: X Y Z : SFinKer
κ : Y ⟶ Z
⊢ X ◁ κ ≫
      { hom := { hom := Kernel.swap X.carrier Z.carrier, property := ⋯ },
          inv := { hom := Kernel.swap Z.carrier X.carrier, property := ⋯ }, hom_inv_id := ⋯, inv_hom_id := ⋯ }.hom =
    { hom := { hom := Kernel.swap X.carrier Y.carrier, property := ⋯ },
          inv := { hom := Kernel.swap Y.carrier X.carrier, property := ⋯ }, hom_inv_id := ⋯, inv_hom_id := ⋯ }.hom ≫
      κ ▷ X

Tactic:
by
    ext : 1; dsimp
    exact Kernel.swap_parallelComp

Goal state after:
Module: Mathlib.Probability.Kernel.Category.SFinKer

Goal state before:
pp: X✝ Y✝ : SFinKer
κ : X✝ ⟶ Y✝
X : SFinKer
⊢ κ ▷ X ≫
      { hom := { hom := Kernel.swap Y✝.carrier X.carrier, property := ⋯ },
          inv := { hom := Kernel.swap X.carrier Y✝.carrier, property := ⋯ }, hom_inv_id := ⋯, inv_hom_id := ⋯ }.hom =
    { hom := { hom := Kernel.swap X✝.carrier X.carrier, property := ⋯ },
          inv := { hom := Kernel.swap X.carrier X✝.carrier, property := ⋯ }, hom_inv_id := ⋯, inv_hom_id := ⋯ }.hom ≫
      X ◁ κ

Tactic:
by
    ext : 1; dsimp
    exact Kernel.swap_parallelComp

Goal state after:
Module: Mathlib.Probability.Process.LocalProperty

Goal state before:
pp: x : ℕ → ℕ
n : ℕ
⊢ mkStrictMonoAux x n < mkStrictMonoAux x (n + 1)

Tactic:
by grind [mkStrictMonoAux]

Goal state after:
Module: Mathlib.Probability.Process.LocalProperty

Goal state before:
pp: x : ℕ → ℕ
⊢ x 0 ≤ mkStrictMonoAux x 0

Tactic:
simp [mkStrictMonoAux]

Goal state after:
Module: Mathlib.Probability.Process.LocalProperty

Goal state before:
pp: x : ℕ → ℕ
n : ℕ
⊢ x (n + 1) ≤ mkStrictMonoAux x (n + 1)

Tactic:
by grind [mkStrictMonoAux]

Goal state after: