mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.Probability.Kernel.Composition.Lemmas

Goal state before:
pp: β : Type u_2
γ : Type u_3
δ : Type u_4
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
mδ : MeasurableSpace δ
β' : Type u_5
ε : Type u_6
mβ' : MeasurableSpace β'
mε : MeasurableSpace ε
κ : Kernel γ β
inst✝² : IsSFiniteKernel κ
η : Kernel ε β'
inst✝¹ : IsSFiniteKernel η
ξ : Kernel (ε × β) δ
inst✝ : IsSFiniteKernel ξ
f : γ → ε
hf : Measurable f
ω : γ
s : Set (δ × β')
hs : MeasurableSet s
⊢ ((ξ ×ₖ prodMkRight β η ∘ₖ (deterministic f hf ×ₖ κ)) ω) s =
    ((ξ ∘ₖ (deterministic f hf ×ₖ κ) ×ₖ (η ∘ₖ deterministic f hf)) ω) s

Tactic:
rw [prod_apply' _ _ _ hs, comp_apply' _ _ _ hs, lintegral_deterministic_prod,
    lintegral_comp, lintegral_deterministic_prod]

Goal state after:
β : Type u_2
γ : Type u_3
δ : Type u_4
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
mδ : MeasurableSpace δ
β' : Type u_5
ε : Type u_6
mβ' : MeasurableSpace β'
mε : MeasurableSpace ε
κ : Kernel γ β
inst✝² : IsSFiniteKernel κ
η : Kernel ε β'
inst✝¹ : IsSFiniteKernel η
ξ : Kernel (ε × β) δ
inst✝ : IsSFiniteKernel ξ
f : γ → ε
hf : Measurable f
ω : γ
s : Set (δ × β')
hs : MeasurableSet s
⊢ ∫⁻ (c : β), ((ξ ×ₖ prodMkRight β η) (f ω, c)) s ∂κ ω =
    ∫⁻ (c : β), ∫⁻ (c : δ), ((η ∘ₖ deterministic f hf) ω) (Prod.mk c ⁻¹' s) ∂ξ (f ω, c) ∂κ ω

case hg
β : Type u_2
γ : Type u_3
δ : Type u_4
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
mδ : MeasurableSpace δ
β' : Type u_5
ε : Type u_6
mβ' : MeasurableSpace β'
mε : MeasurableSpace ε
κ : Kernel γ β
inst✝² : IsSFiniteKernel κ
η : Kernel ε β'
inst✝¹ : IsSFiniteKernel η
ξ : Kernel (ε × β) δ
inst✝ : IsSFiniteKernel ξ
f : γ → ε
hf : Measurable f
ω : γ
s : Set (δ × β')
hs : MeasurableSet s
⊢ Measurable fun b ↦ ∫⁻ (c : δ), ((η ∘ₖ deterministic f hf) ω) (Prod.mk c ⁻¹' s) ∂ξ b

case hg
β : Type u_2
γ : Type u_3
δ : Type u_4
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
mδ : MeasurableSpace δ
β' : Type u_5
ε : Type u_6
mβ' : MeasurableSpace β'
mε : MeasurableSpace ε
κ : Kernel γ β
inst✝² : IsSFiniteKernel κ
η : Kernel ε β'
inst✝¹ : IsSFiniteKernel η
ξ : Kernel (ε × β) δ
inst✝ : IsSFiniteKernel ξ
f : γ → ε
hf : Measurable f
ω : γ
s : Set (δ × β')
hs : MeasurableSet s
⊢ Measurable fun b ↦ ((η ∘ₖ deterministic f hf) ω) (Prod.mk b ⁻¹' s)

case hg
β : Type u_2
γ : Type u_3
δ : Type u_4
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
mδ : MeasurableSpace δ
β' : Type u_5
ε : Type u_6
mβ' : MeasurableSpace β'
mε : MeasurableSpace ε
κ : Kernel γ β
inst✝² : IsSFiniteKernel κ
η : Kernel ε β'
inst✝¹ : IsSFiniteKernel η
ξ : Kernel (ε × β) δ
inst✝ : IsSFiniteKernel ξ
f : γ → ε
hf : Measurable f
ω : γ
s : Set (δ × β')
hs : MeasurableSet s
⊢ Measurable fun b ↦ ((ξ ×ₖ prodMkRight β η) b) s
Module: Mathlib.Probability.Kernel.Composition.Lemmas

Goal state before:
pp: case hf
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
μ : Measure α
ν : Measure β
inst✝¹ : SFinite ν
κ : Kernel β γ
inst✝ : IsSFiniteKernel κ
s : Set (α × γ)
hs : MeasurableSet s
⊢ AEMeasurable (fun a ↦ ((Kernel.id ∥ₖ κ) a) s) (μ.prod ν)

Tactic:
exact (Kernel.measurable_coe _ hs).aemeasurable

Goal state after:
Module: Mathlib.Probability.Kernel.Disintegration.Basic

Goal state before:
pp: α : Type u_1
β : Type u_2
Ω : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mΩ : MeasurableSpace Ω
κ : Kernel α (β × Ω)
κCond : Kernel (α × β) Ω
inst✝¹ : IsFiniteKernel κ.fst
inst✝ : κ.IsCondKernel κCond
a : α
h : κ.fst ⊗ₖ κCond = κ
⊢ ∀ᵐ (b : β) ∂κ.fst a, IsProbabilityMeasure (κCond (a, b))

Tactic:
by_cases h_sfin : IsSFiniteKernel κCond

Goal state after:
case pos
α : Type u_1
β : Type u_2
Ω : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mΩ : MeasurableSpace Ω
κ : Kernel α (β × Ω)
κCond : Kernel (α × β) Ω
inst✝¹ : IsFiniteKernel κ.fst
inst✝ : κ.IsCondKernel κCond
a : α
h : κ.fst ⊗ₖ κCond = κ
h_sfin : IsSFiniteKernel κCond
⊢ ∀ᵐ (b : β) ∂κ.fst a, IsProbabilityMeasure (κCond (a, b))

case neg
α : Type u_1
β : Type u_2
Ω : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mΩ : MeasurableSpace Ω
κ : Kernel α (β × Ω)
κCond : Kernel (α × β) Ω
inst✝¹ : IsFiniteKernel κ.fst
inst✝ : κ.IsCondKernel κCond
a : α
h : κ.fst ⊗ₖ κCond = κ
h_sfin : ¬IsSFiniteKernel κCond
⊢ ∀ᵐ (b : β) ∂κ.fst a, IsProbabilityMeasure (κCond (a, b))
Module: Mathlib.Probability.Kernel.Disintegration.CDFToKernel

Goal state before:
pp: case basic
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)
t₁ : Set β
ht₁ : MeasurableSet t₁
t₂ : Set ℝ
ht₂ : MeasurableSet t₂
⊢ ∫⁻ (b : β), ((IsCondKernelCDF.toKernel f hf) (a, b)) (Prod.mk b ⁻¹' (fun x1 x2 ↦ x1 ×ˢ x2) t₁ t₂) ∂ν a =
    (κ a) ((fun x1 x2 ↦ x1 ×ˢ x2) t₁ t₂)

Tactic:
rw [← lintegral_add_compl _ ht₁]

Goal state after:
case basic
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)
t₁ : Set β
ht₁ : MeasurableSet t₁
t₂ : Set ℝ
ht₂ : MeasurableSet t₂
⊢ ∫⁻ (x : β) in t₁, ((IsCondKernelCDF.toKernel f hf) (a, x)) (Prod.mk x ⁻¹' (fun x1 x2 ↦ x1 ×ˢ x2) t₁ t₂) ∂ν a +
      ∫⁻ (x : β) in t₁ᶜ, ((IsCondKernelCDF.toKernel f hf) (a, x)) (Prod.mk x ⁻¹' (fun x1 x2 ↦ x1 ×ˢ x2) t₁ t₂) ∂ν a =
    (κ a) ((fun x1 x2 ↦ x1 ×ˢ x2) t₁ t₂)
Module: Mathlib.Probability.Kernel.Disintegration.Integral

Goal state before:
pp: α : Type u_1
β : Type u_2
Ω : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
inst✝⁴ : MeasurableSpace Ω
inst✝³ : StandardBorelSpace Ω
inst✝² : Nonempty Ω
inst✝¹ : CountableOrCountablyGenerated α β
κ : Kernel α (β × Ω)
inst✝ : IsFiniteKernel κ
f : β × Ω → ℝ≥0∞
hf : Measurable f
a : α
s : Set β
hs : MeasurableSet s
t : Set Ω
ht : MeasurableSet t
⊢ ∫⁻ (b : β) in s, ∫⁻ (ω : Ω) in t, f (b, ω) ∂κ.condKernel (a, b) ∂κ.fst a = ∫⁻ (x : β × Ω) in s ×ˢ t, f x ∂κ a

Tactic:
conv_rhs => rw [← κ.disintegrate κ.condKernel]
  rw [Kernel.setLIntegral_compProd _ _ _ hf hs ht]

Goal state after:
Module: Mathlib.Probability.Kernel.Disintegration.Density

Goal state before:
pp: case refine_3
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
i j : ℕ
hij : i ≤ j
a : α
s : Set β
hs : MeasurableSet s
⊢ ∀ (s_1 : Set γ),
    MeasurableSet s_1 →
      (ν a) s_1 < ∞ →
        ∫ (x : γ) in s_1, κ.densityProcess ν i a x s ∂ν a = ∫ (x : γ) in s_1, κ.densityProcess ν j a x s ∂ν a

Tactic:
intro x hx _

Goal state after:
case refine_3
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
i j : ℕ
hij : i ≤ j
a : α
s : Set β
hs : MeasurableSet s
x : Set γ
hx : MeasurableSet x
a✝ : (ν a) x < ∞
⊢ ∫ (x : γ) in x, κ.densityProcess ν i a x s ∂ν a = ∫ (x : γ) in x, κ.densityProcess ν j a x s ∂ν a
Module: Mathlib.Probability.Kernel.Disintegration.Density

Goal state before:
pp: α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
inst✝ : IsFiniteKernel κ
n : ℕ
a : α
x : γ
seq : ℕ → Set β
hseq : Antitone seq
hseq_iInter : ⋂ i, seq i = ∅
hseq_meas : ∀ (m : ℕ), MeasurableSet (seq m)
⊢ Tendsto (fun m ↦ κ.densityProcess ν n a x (seq m)) atTop (𝓝 (κ.densityProcess ν n a x ∅))

Tactic:
simp_rw [densityProcess]
  by_cases h0 : ν a (countablePartitionSet n x) = 0
  · simp_rw [h0, ENNReal.toReal_div]
    simp
  refine (ENNReal.tendsto_toReal ?_).comp ?_
  · rw [ne_eq, ENNReal.div_eq_top]
    push Not
    simp
  refine ENNReal.Tendsto.div_const ?_ (.inr h0)
  have : Tendsto (fun m ↦ κ a (countablePartitionSet n x ×ˢ seq m)) atTop
      (𝓝 ((κ a) (⋂ n_1, countablePartitionSet n x ×ˢ seq n_1))) := by
    apply tendsto_measure_iInter_atTop
    · measurability
    · exact fun _ _ h ↦ prod_mono_right <| hseq h
    · exact ⟨0, measure_ne_top _ _⟩
  simpa only [← prod_iInter, hseq_iInter] using this

Goal state after:
Module: Mathlib.Probability.Kernel.Disintegration.Density

Goal state before:
pp: α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
a : α
s : Set β
hs : MeasurableSet s
n : ℕ
⊢ (ν a) univ = ↑((ν a) univ).toNNReal

Tactic:
rw [ENNReal.coe_toNNReal]

Goal state after:
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
a : α
s : Set β
hs : MeasurableSet s
n : ℕ
⊢ (ν a) univ ≠ ∞
Module: Mathlib.Probability.Kernel.CondDistrib

Goal state before:
pp: α : Type u_1
β : Type u_2
Ω : Type u_3
F : Type u_4
inst✝⁵ : MeasurableSpace Ω
inst✝⁴ : StandardBorelSpace Ω
inst✝³ : Nonempty Ω
inst✝² : NormedAddCommGroup F
mα : MeasurableSpace α
μ : Measure α
inst✝¹ : IsFiniteMeasure μ
X : α → β
Y : α → Ω
mβ : MeasurableSpace β
f : β × Ω → F
inst✝ : NormedSpace ℝ F
hY : AEMeasurable Y μ
hf : AEStronglyMeasurable f (Measure.map (fun a ↦ (X a, Y a)) μ)
⊢ AEStronglyMeasurable (fun x ↦ ∫ (y : Ω), f (x, y) ∂(Measure.map (fun a ↦ (X a, Y a)) μ).condKernel x)
    (Measure.map (fun a ↦ (X a, Y a)) μ).fst

Tactic:
exact hf.integral_condKernel

Goal state after:
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✝⁴ : CountablyGenerated γ
mΩ : MeasurableSpace Ω
inst✝³ : StandardBorelSpace Ω
inst✝² : Nonempty Ω
ρ✝ : Measure (α × Ω)
inst✝¹ : IsFiniteMeasure ρ✝
ρ : Measure (α × Ω)
inst✝ : IsFiniteMeasure ρ
h1 : const Unit ρ.fst = (const Unit ρ).fst
a✝¹ : Unit × α
s✝ : Set Ω
a✝ : MeasurableSet s✝
⊢ ((prodMkLeft Unit ρ.condKernel) a✝¹) s✝ = ((const Unit ρ).condKernelUnitBorel a✝¹) s✝

Tactic:
simp only [prodMkLeft_apply, Measure.condKernel_apply]

Goal state after:
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
h : CountableOrCountablyGenerated α β
κ : Kernel α (β × Ω)
inst✝ : IsFiniteKernel κ
⊢ IsMarkovKernel κ.condKernel

Tactic:
rw [condKernel_def]
  split_ifs <;> infer_instance

Goal state after:
Module: Mathlib.Probability.CondVar

Goal state before:
pp: case inr
Ω : Type u_1
m₀ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
hμ : NeZero μ
⊢ Var[X; μ | ⊥] =ᵐ[μ] fun x ↦ ⨍ (ω : Ω), (X ω - ⨍ (ω' : Ω), X ω' ∂μ) ^ 2 ∂μ

Tactic:
exact .of_forall <| congr_fun (condVar_bot' X)

Goal state after:
Module: Mathlib.Probability.ProductMeasure

Goal state before:
pp: case neg
X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
μ : (n : ℕ) → Measure (X n)
hμ : ∀ (n : ℕ), IsProbabilityMeasure (μ n)
a b : ℕ
hba : b ≤ a
this : IsEmpty ↥(Ioc a b)
x : (i : ↥(Iic a)) → X ↑i
s : Set ((i : ↥(Ioc a b)) → X ↑i)
ms : MeasurableSet s
hs : ¬s.Nonempty
⊢ (((partialTraj (fun n ↦ const ((i : ↥(Iic n)) → X ↑i) (μ (n + 1))) a b).map (restrict₂ ⋯)) x) s =
    ((const ((i : ↥(Iic a)) → X ↑i) (Measure.pi fun i ↦ μ ↑i)) x) s

Tactic:
rw [Set.not_nonempty_iff_eq_empty.1 hs]
      simp

Goal state after:
Module: Mathlib.Probability.ProductMeasure

Goal state before:
pp: X : ℕ → Type u_1
mX : (n : ℕ) → MeasurableSpace (X n)
μ : (n : ℕ) → Measure (X n)
hμ : ∀ (n : ℕ), IsProbabilityMeasure (μ n)
a b : ℕ
⊢ partialTraj (fun n ↦ const ((i : ↥(Iic n)) → X ↑i) (μ (n + 1))) a b =
    (Kernel.id ×ₖ const ((i : ↥(Iic a)) → X ↑i) (Measure.pi fun i ↦ μ ↑i)).map (_root_.IicProdIoc a b)

Tactic:
rw [partialTraj_eq_prod, partialTraj_const_restrict₂]

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
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
κ η : Kernel α γ
a : α
x : γ
⊢ κ.rnDeriv η a x = ∞ ↔ x ∈ κ.mutuallySingularSetSlice η a

Tactic:
rnDeriv_eq_top_iff,

Goal state after:
α : Type u_1
γ : Type u_2
mα : MeasurableSpace α
mγ : MeasurableSpace γ
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
κ η : Kernel α γ
a : α
x : γ
⊢ (a, x) ∈ κ.mutuallySingularSet η ↔ x ∈ κ.mutuallySingularSetSlice η a
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)
A : ℕ → Set ((i : ι) → X i)
A_mem : ∀ (n : ℕ), A n ∈ measurableCylinders X
A_anti : Antitone A
A_inter : ⋂ n, A n = ∅
this : ∀ (i : ι), Nonempty (X i)
s : ℕ → Finset ι
S : (n : ℕ) → Set ((i : ↥(s n)) → X ↑i)
mS : ∀ (n : ℕ), MeasurableSet (S n)
A_eq : ∀ (n : ℕ), A n = cylinder (s n) (S n)
u : Set ι := ⋃ n, ↑(s n)
t : ℕ → Finset ↑u := fun n ↦ (s n).preimage Subtype.val ⋯
f : ((i : ↑u) → X ↑i) → (i : ι) → X i := fun x i ↦ if hi : i ∈ u then x ⟨i, hi⟩ else Classical.ofNonempty
aux : (n : ℕ) → ↥(t n) ≃ ↥(s n) :=
  fun n ↦ { toFun := fun i ↦ ⟨↑↑i, ⋯⟩, invFun := fun i ↦ ⟨⟨↑i, ⋯⟩, ⋯⟩, left_inv := ⋯, right_inv := ⋯ }
g : (n : ℕ) → ((a : ↥(t n)) → X ↑((aux n) a)) ≃ ((b : ↥(s n)) → X ↑b) :=
  fun n ↦ Equiv.piCongrLeft (fun i ↦ X ↑i) (aux n)
r_comp_f : ∀ (n : ℕ), (s n).restrict ∘ f = ⇑(g n) ∘ fun x i ↦ x ↑i
B : ℕ → Set ((i : ↑u) → X ↑i) := fun n ↦ f ⁻¹' A n
T : (n : ℕ) → Set ((a : ↥(t n)) → X ↑((aux n) a)) := fun n ↦ ⇑(g n) ⁻¹' S n
B_eq : ∀ (n : ℕ), B n = cylinder (t n) (T n)
mT : ∀ (n : ℕ), MeasurableSet (T n)
B_mem : ∀ (n : ℕ), B n ∈ measurableCylinders fun i ↦ X ↑i
mB : ∀ (n : ℕ), MeasurableSet (B n)
B_anti : Antitone B
⊢ ⋂ n, B n = ∅

Tactic:
simp_rw [B, ← Set.preimage_iInter, A_inter, Set.preimage_empty]

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 η : Kernel α γ
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
inst✝¹ : IsFiniteKernel κ
inst✝ : IsFiniteKernel η
a : α
h : κ a ≪ η a
⊢ (η a).withDensity (κ.rnDeriv η a) = κ a

Tactic:
have h_ae := κ.rnDeriv_eq_rnDeriv_measure (η := η) (a := a)

Goal state after:
α : Type u_1
γ : Type u_2
mα : MeasurableSpace α
mγ : MeasurableSpace γ
κ η : Kernel α γ
hαγ : MeasurableSpace.CountableOrCountablyGenerated α γ
inst✝¹ : IsFiniteKernel κ
inst✝ : IsFiniteKernel η
a : α
h : κ a ≪ η a
h_ae : κ.rnDeriv η a =ᵐ[η a] ∂κ a/∂η a
⊢ (η a).withDensity (κ.rnDeriv η a) = κ a
Module: Mathlib.Probability.ProductMeasure

Goal state before:
pp: ι : Type u_3
κ : ι → Type u_4
X : (i : ι) → κ i → Type u_5
mX : (i : ι) → (j : κ i) → MeasurableSpace (X i j)
μ : (i : ι) → (j : κ i) → Measure (X i j)
hμ : ∀ (i : ι) (j : κ i), IsProbabilityMeasure (μ i j)
s : Finset ((i : ι) × κ i)
t : (i : (i : ι) × κ i) → Set (X i.fst i.snd)
ht : ∀ (i : (i : ι) × κ i), MeasurableSet (t i)
⊢ (map (⇑(piCurry X).symm) (infinitePi fun i ↦ infinitePi fun j ↦ μ i j)) ((↑s).pi t) = ∏ i ∈ s, (μ i.fst i.snd) (t i)

Tactic:
classical
  rw [map_apply (by fun_prop) (.pi (countable_toSet _) fun _ _ ↦ ht _),
    ← Finset.sigma_image_fst_preimage_mk s, coe_piCurry_symm, Finset.coe_sigma,
    Set.uncurry_preimage_sigma_pi, infinitePi_pi, Finset.prod_sigma]
  · exact Finset.prod_congr rfl (fun _ _ ↦ infinitePi_pi _ fun _ _ ↦ ht _)
  · simp only [mem_image, Sigma.exists, exists_and_right, exists_eq_right, forall_exists_index]
    exact fun i j hij ↦ MeasurableSet.pi (countable_toSet _) fun k hk ↦ by simp_all

Goal state after:
Module: Mathlib.Probability.Decision.Risk.Basic

Goal state before:
pp: case inr
Θ : Type u_1
𝓧 : Type u_2
𝓨 : Type u_4
mΘ : MeasurableSpace Θ
m𝓧 : MeasurableSpace 𝓧
m𝓨 : MeasurableSpace 𝓨
ℓ : Θ → 𝓨 → ℝ≥0∞
P : Kernel Θ 𝓧
π : Measure Θ
inst✝¹ : Subsingleton 𝓧
inst✝ : Nonempty 𝓨
hX : Nonempty 𝓧
x : 𝓧
e : { κ // IsMarkovKernel κ } ≃ { μ // IsProbabilityMeasure μ } :=
  { toFun := fun κ ↦ ⟨↑κ x, ⋯⟩, invFun := fun μ ↦ ⟨Kernel.const 𝓧 ↑μ, ⋯⟩, left_inv := ⋯, right_inv := ⋯ }
⊢ ⨅ x, avgRisk ℓ P (↑x) π = ⨅ x, avgRisk ℓ P (Kernel.const 𝓧 ↑x) π

Tactic:
rw [← Equiv.iInf_comp e.symm]

Goal state after:
case inr
Θ : Type u_1
𝓧 : Type u_2
𝓨 : Type u_4
mΘ : MeasurableSpace Θ
m𝓧 : MeasurableSpace 𝓧
m𝓨 : MeasurableSpace 𝓨
ℓ : Θ → 𝓨 → ℝ≥0∞
P : Kernel Θ 𝓧
π : Measure Θ
inst✝¹ : Subsingleton 𝓧
inst✝ : Nonempty 𝓨
hX : Nonempty 𝓧
x : 𝓧
e : { κ // IsMarkovKernel κ } ≃ { μ // IsProbabilityMeasure μ } :=
  { toFun := fun κ ↦ ⟨↑κ x, ⋯⟩, invFun := fun μ ↦ ⟨Kernel.const 𝓧 ↑μ, ⋯⟩, left_inv := ⋯, right_inv := ⋯ }
⊢ ⨅ x, avgRisk ℓ P (↑(e.symm x)) π = ⨅ x, avgRisk ℓ P (Kernel.const 𝓧 ↑x) π
Module: Mathlib.Probability.Distributions.Gamma

Goal state before:
pp: a r : ℝ
ha : 0 < a
hr : 0 < r
x : ℝ
⊢ 0 ≤ gammaPDFReal a r x

Tactic:
unfold gammaPDFReal
  split_ifs <;> positivity

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

Goal state before:
pp: α : Type u_1
p : PMF α
a : α
h : p a = 1
a' : α
ha' : a' ∈ p.support
ha : a' ∉ {a}
⊢ 0 < ∑' (b : α), if b = a then 0 else p b

Tactic:
pos_iff_ne_zero,

Goal state after:
α : Type u_1
p : PMF α
a : α
h : p a = 1
a' : α
ha' : a' ∈ p.support
ha : a' ∉ {a}
⊢ (∑' (b : α), if b = a then 0 else p b) ≠ 0
Module: Mathlib.Probability.ProbabilityMassFunction.Basic

Goal state before:
pp: α : Type u_1
p : PMF α
⊢ p.toOuterMeasure.caratheodory = ⊤

Tactic:
by
  refine eq_top_iff.2 <| le_trans (le_sInf fun x hx => ?_) (le_sum_caratheodory _)
  have ⟨y, hy⟩ := hx
  exact
    ((le_of_eq (dirac_caratheodory y).symm).trans (le_smul_caratheodory _ _)).trans (le_of_eq hy)

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

Goal state before:
pp: r : ℝ
hr : 0 < r
x : ℝ
h : 0 ≤ x
⊢ ∫ (a : ℝ) in uIoc 0 x, r * rexp (-(r * a)) = ∫ (a : ℝ) in 0..x, r * rexp (-(r * a))

Tactic:
rw [intervalIntegral.intervalIntegral_eq_integral_uIoc, smul_eq_mul, if_pos h, one_mul]

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

Goal state before:
pp: case false
p : ℝ≥0
h : p ≤ 1
⊢ ¬1 - ↑p = 0 ↔ ¬p = 1

Tactic:
not_iff_not

Goal state after:
case false
p : ℝ≥0
h : p ≤ 1
⊢ 1 - ↑p = 0 ↔ p = 1
Module: Mathlib.Probability.Distributions.Uniform

Goal state before:
pp: α : Type u_1
s : Finset α
hs : s.Nonempty
t : Set α
x : α
hx : x ∈ {x ∈ s | x ∈ t}
this : x ∈ s ∧ x ∈ t
⊢ (if x ∈ s ∧ x ∈ t then (↑(#s))⁻¹ else 0) = (↑(#s))⁻¹

Tactic:
simp only [this, and_self_iff, if_true]

Goal state after:
Module: Mathlib.Probability.Independence.Conditional

Goal state before:
pp: Ω : Type u_1
ι : Type u_2
m' mΩ : MeasurableSpace Ω
inst✝¹ : StandardBorelSpace Ω
hm' : m' ≤ mΩ
m : ι → MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
⊢ iCondIndep m' hm' m μ ↔ iCondIndepSets m' hm' (fun x ↦ {s | MeasurableSet s}) μ

Tactic:
by
  simp only [iCondIndep, iCondIndepSets, Kernel.iIndep]

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

Goal state before:
pp: X Y Z : SFinKer
⊢ Kernel.deterministic ⇑MeasurableEquiv.prodAssoc ⋯ ∘ₖ Kernel.swap X.carrier (Y.carrier × Z.carrier) ∘ₖ
      Kernel.deterministic ⇑MeasurableEquiv.prodAssoc ⋯ =
    Kernel.id ∥ₖ Kernel.swap X.carrier Z.carrier ∘ₖ Kernel.deterministic ⇑MeasurableEquiv.prodAssoc ⋯ ∘ₖ
      (Kernel.swap X.carrier Y.carrier ∥ₖ Kernel.id)

Tactic:
simp only [Kernel.id, Kernel.swap]

Goal state after:
X Y Z : SFinKer
⊢ Kernel.deterministic ⇑MeasurableEquiv.prodAssoc ⋯ ∘ₖ Kernel.deterministic Prod.swap ⋯ ∘ₖ
      Kernel.deterministic ⇑MeasurableEquiv.prodAssoc ⋯ =
    Kernel.deterministic id ⋯ ∥ₖ Kernel.deterministic Prod.swap ⋯ ∘ₖ
        Kernel.deterministic ⇑MeasurableEquiv.prodAssoc ⋯ ∘ₖ
      (Kernel.deterministic Prod.swap ⋯ ∥ₖ Kernel.deterministic id ⋯)
Module: Mathlib.Probability.Kernel.Category.SFinKer

Goal state before:
pp: X Y Z : SFinKer
⊢ Kernel.deterministic ⇑MeasurableEquiv.prodAssoc.symm ⋯ ∘ₖ Kernel.swap (X.carrier × Y.carrier) Z.carrier ∘ₖ
      Kernel.deterministic ⇑MeasurableEquiv.prodAssoc.symm ⋯ =
    Kernel.swap X.carrier Z.carrier ∥ₖ Kernel.id ∘ₖ Kernel.deterministic ⇑MeasurableEquiv.prodAssoc.symm ⋯ ∘ₖ
      (Kernel.id ∥ₖ Kernel.swap Y.carrier Z.carrier)

Tactic:
simp only [Kernel.id, Kernel.swap]

Goal state after:
X Y Z : SFinKer
⊢ Kernel.deterministic ⇑MeasurableEquiv.prodAssoc.symm ⋯ ∘ₖ Kernel.deterministic Prod.swap ⋯ ∘ₖ
      Kernel.deterministic ⇑MeasurableEquiv.prodAssoc.symm ⋯ =
    Kernel.deterministic Prod.swap ⋯ ∥ₖ Kernel.deterministic id ⋯ ∘ₖ
        Kernel.deterministic ⇑MeasurableEquiv.prodAssoc.symm ⋯ ∘ₖ
      (Kernel.deterministic id ⋯ ∥ₖ Kernel.deterministic Prod.swap ⋯)
Module: Mathlib.Probability.Independence.Conditional

Goal state before:
pp: Ω : Type u_1
ι : Type u_2
m' mΩ : MeasurableSpace Ω
inst✝¹ : StandardBorelSpace Ω
hm' : m' ≤ mΩ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
β : ι → Type u_5
m : (i : ι) → MeasurableSpace (β i)
f : (i : ι) → Ω → β i
h_indep : iCondIndepFun m' hm' f μ
hf : ∀ (i : ι), Measurable (f i)
i j k l : ι
hik : i ≠ k
hil : i ≠ l
hjk : j ≠ k
hjl : j ≠ l
⊢ CondIndepFun m' hm' (fun a ↦ (f i a, f j a)) (fun a ↦ (f k a, f l a)) μ

Tactic:
let g (i j : ι) (v : Π x : ({i, j} : Finset ι), β x) : β i × β j :=
    ⟨v ⟨i, mem_insert_self _ _⟩, v ⟨j, mem_insert_of_mem <| mem_singleton_self _⟩⟩

Goal state after:
Ω : Type u_1
ι : Type u_2
m' mΩ : MeasurableSpace Ω
inst✝¹ : StandardBorelSpace Ω
hm' : m' ≤ mΩ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
β : ι → Type u_5
m : (i : ι) → MeasurableSpace (β i)
f : (i : ι) → Ω → β i
h_indep : iCondIndepFun m' hm' f μ
hf : ∀ (i : ι), Measurable (f i)
i j k l : ι
hik : i ≠ k
hil : i ≠ l
hjk : j ≠ k
hjl : j ≠ l
g : (i j : ι) → ((x : ↥{i, j}) → β ↑x) → β i × β j := fun i j v ↦ (v ⟨i, ⋯⟩, v ⟨j, ⋯⟩)
⊢ CondIndepFun m' hm' (fun a ↦ (f i a, f j a)) (fun a ↦ (f k a, f l a)) μ
Module: Mathlib.Probability.Moments.SubGaussian

Goal state before:
pp: case pos
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
h : HasSubgaussianMGF X c κ ν
ε : ℝ
hε : 0 ≤ ε
hc0 : c = 0
⊢ ∀ᵐ (ω' : Ω') ∂ν, (κ ω').real {ω | ε ≤ X ω} ≤ rexp (-ε ^ 2 / (2 * ↑c))

Tactic:
filter_upwards [h.measure_univ_le_one] with ω' h

Goal state after:
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
h✝ : HasSubgaussianMGF X c κ ν
ε : ℝ
hε : 0 ≤ ε
hc0 : c = 0
ω' : Ω'
h : (κ ω') Set.univ ≤ 1
⊢ (κ ω').real {ω | ε ≤ X ω} ≤ rexp (-ε ^ 2 / (2 * ↑c))