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

Goal state before:
pp: case refine_2
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 → ℚ → ℝ
hf : IsRatCondKernelCDFAux f κ ν
inst✝ : IsFiniteKernel ν
a : α
seq : ℕ → ℚ
hseq : Monotone seq
hseq_tendsto : Tendsto seq atTop atTop
⊢ Tendsto (fun i ↦ ∫ (a_1 : β), f (a, a_1) (seq i) ∂ν a) atTop (𝓝 (∫ (a : β), 1 ∂ν a))

Tactic:
MeasureTheory.integral_const,

Goal state after:
case refine_2
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 → ℚ → ℝ
hf : IsRatCondKernelCDFAux f κ ν
inst✝ : IsFiniteKernel ν
a : α
seq : ℕ → ℚ
hseq : Monotone seq
hseq_tendsto : Tendsto seq atTop atTop
⊢ Tendsto (fun i ↦ ∫ (a_1 : β), f (a, a_1) (seq i) ∂ν a) atTop (𝓝 ((ν a).real univ • 1))
Module: Mathlib.Probability.Kernel.Disintegration.CondCDF

Goal state before:
pp: α : Type u_1
mα : MeasurableSpace α
ρ : Measure (α × ℝ)
a : α
⊢ (condCDF ρ a).measure univ = 1

Tactic:
rw [← ENNReal.ofReal_one, ← sub_zero (1 : ℝ)]
  exact StieltjesFunction.measure_univ _ (tendsto_condCDF_atBot ρ a) (tendsto_condCDF_atTop ρ a)

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

Goal state before:
pp: 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
hs : MeasurableSet s
h_dir : Directed (fun x y ↦ x ⊆ y) fun q ↦ Iic ↑q
⊢ Directed (fun x y ↦ x ⊆ y) fun q ↦ s ×ˢ Iic ↑q

Tactic:
refine Monotone.directed_le fun i j hij ↦ ?_

Goal state after:
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
hs : MeasurableSet s
h_dir : Directed (fun x y ↦ x ⊆ y) fun q ↦ Iic ↑q
i j : ℚ
hij : i ≤ j
⊢ s ×ˢ Iic ↑i ⊆ s ×ˢ Iic ↑j
Module: Mathlib.Probability.Kernel.Composition.Lemmas

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
μ : Measure α
κ : Kernel α β
inst✝¹ : SFinite μ
inst✝ : IsSFiniteKernel κ
f : β → γ
hf : Measurable f
⊢ μ ⊗ₘ κ.map f = ⇑(Kernel.id ∥ₖ Kernel.deterministic f hf) ∘ₘ ⇑(Kernel.id ×ₖ κ) ∘ₘ μ

Tactic:
rw [comp_assoc, Kernel.parallelComp_comp_prod, compProd_eq_comp_prod,
      Kernel.id_comp, Kernel.deterministic_comp_eq_map]

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✝ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
n : ℕ
s : Set β
hs : MeasurableSet s
⊢ Measurable fun p ↦ (κ p.1) (countablePartitionSet n p.2 ×ˢ s) / (ν p.1) (countablePartitionSet n p.2)

Tactic:
by
  change Measurable[mα.prod (countableFiltration γ n)]
      ((fun (p : α × countablePartition γ n) ↦ κ p.1 (↑p.2 ×ˢ s) / ν p.1 p.2)
        ∘ (fun (p : α × γ) ↦ (p.1, ⟨countablePartitionSet n p.2, countablePartitionSet_mem n p.2⟩)))
  have h1 : @Measurable _ _ (mα.prod ⊤) _
      (fun p : α × countablePartition γ n ↦ κ p.1 (↑p.2 ×ˢ s) / ν p.1 p.2) := by
    refine Measurable.div ?_ ?_
    · refine measurable_from_prod_countable_left (fun t ↦ ?_)
      exact Kernel.measurable_coe _ ((measurableSet_countablePartition _ t.prop).prod hs)
    · refine measurable_from_prod_countable_left ?_
      rintro ⟨t, ht⟩
      exact Kernel.measurable_coe _ (measurableSet_countablePartition _ ht)
  refine h1.comp (measurable_fst.prodMk ?_)
  change @Measurable (α × γ) (countablePartition γ n) (mα.prod (countableFiltration γ n)) ⊤
    ((fun c ↦ ⟨countablePartitionSet n c, countablePartitionSet_mem n c⟩) ∘ (fun p : α × γ ↦ p.2))
  exact (measurable_countablePartitionSet_subtype n ⊤).comp measurable_snd

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

Goal state before:
pp: 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)
⊢ ∀ (a : β), Prod.mk a ⁻¹' ⋃ i, f' i = ⋃ i, Prod.mk a ⁻¹' f' i

Tactic:
simp only [preimage_iUnion, implies_true]

Goal state after:
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
t : Set Ω
ht : MeasurableSet t
⊢ ∫⁻ (b : β), ∫⁻ (ω : Ω) in t, f (b, ω) ∂κ.condKernel (a, b) ∂κ.fst a = ∫⁻ (x : β × Ω) in Set.univ ×ˢ t, f x ∂κ a

Tactic:
by
  rw [← setLIntegral_condKernel hf a MeasurableSet.univ ht]; simp_rw [Measure.restrict_univ]

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

Goal state before:
pp: α : Type u_1
β : Type u_2
Ω : Type u_3
inst✝⁴ : MeasurableSpace Ω
inst✝³ : StandardBorelSpace Ω
inst✝² : Nonempty Ω
mα : MeasurableSpace α
μ : Measure α
inst✝¹ : IsFiniteMeasure μ
X : α → β
Y : α → Ω
mβ : MeasurableSpace β
inst✝ : MeasurableSingletonClass β
hY : Measurable Y
x : β
hX : (Measure.map X μ) {x} ≠ 0
s : Set Ω
⊢ ((Measure.map (fun a ↦ (X a, Y a)) μ).fst {x})⁻¹ * (Measure.map (fun a ↦ (X a, Y a)) μ) ({x} ×ˢ s) =
    ((Measure.map X μ) {x})⁻¹ * (Measure.map (fun a ↦ (X a, Y a)) μ) ({x} ×ˢ s)

pp: α : Type u_1
β : Type u_2
Ω : Type u_3
inst✝⁴ : MeasurableSpace Ω
inst✝³ : StandardBorelSpace Ω
inst✝² : Nonempty Ω
mα : MeasurableSpace α
μ : Measure α
inst✝¹ : IsFiniteMeasure μ
X : α → β
Y : α → Ω
mβ : MeasurableSpace β
inst✝ : MeasurableSingletonClass β
hY : Measurable Y
x : β
hX : (Measure.map X μ) {x} ≠ 0
s : Set Ω
⊢ (Measure.map (fun a ↦ (X a, Y a)) μ).fst {x} ≠ 0

Tactic:
· rw [Measure.fst_map_prodMk hY]

Goal state after:
α : Type u_1
β : Type u_2
Ω : Type u_3
inst✝⁴ : MeasurableSpace Ω
inst✝³ : StandardBorelSpace Ω
inst✝² : Nonempty Ω
mα : MeasurableSpace α
μ : Measure α
inst✝¹ : IsFiniteMeasure μ
X : α → β
Y : α → Ω
mβ : MeasurableSpace β
inst✝ : MeasurableSingletonClass β
hY : Measurable Y
x : β
hX : (Measure.map X μ) {x} ≠ 0
s : Set Ω
⊢ (Measure.map (fun a ↦ (X a, Y a)) μ).fst {x} ≠ 0
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
A : Set γ
hA : MeasurableSet A
⊢ ∫⁻ (x : γ) in A, ENNReal.ofReal (κ.density ν a x s) ∂ν a = (κ a) (A ×ˢ s)

Tactic:
by
  have : IsFiniteKernel κ := isFiniteKernel_of_isFiniteKernel_fst (h := isFiniteKernel_of_le hκν)
  rw [← ofReal_integral_eq_lintegral_ofReal]
  · rw [setIntegral_density hκν a hs hA, measureReal_def,
      ENNReal.ofReal_toReal (measure_ne_top _ _)]
  · exact (integrable_density hκν a hs).restrict
  · exact ae_of_all _ (fun _ ↦ density_nonneg hκν _ _ _)

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

Goal state before:
pp: case refine_2
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)
inst✝ : IsFiniteKernel κ
a : α
seq : ℕ → Set β
hseq : Monotone seq
hseq_iUnion : ⋃ i, seq i = univ
hseq_meas : ∀ (m : ℕ), MeasurableSet (seq m)
⊢ Tendsto (fun i ↦ ∫ (a_1 : γ), κ.density κ.fst a a_1 (seq i) ∂κ.fst a) atTop (𝓝 (∫ (a : γ), 1 ∂κ.fst a))

Tactic:
MeasureTheory.integral_const,

Goal state after:
case refine_2
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)
inst✝ : IsFiniteKernel κ
a : α
seq : ℕ → Set β
hseq : Monotone seq
hseq_iUnion : ⋃ i, seq i = univ
hseq_meas : ∀ (m : ℕ), MeasurableSet (seq m)
⊢ Tendsto (fun i ↦ ∫ (a_1 : γ), κ.density κ.fst a a_1 (seq i) ∂κ.fst a) atTop (𝓝 ((κ.fst a).real univ • 1))
Module: Mathlib.Probability.CondVar

Goal state before:
pp: Ω : Type u_1
m₀ m : MeasurableSpace Ω
μ : Measure Ω
hm : m ≤ m₀
c : ℝ
⊢ Var[fun x ↦ c; μ | m] = 0

Tactic:
obtain rfl | hc := eq_or_ne c 0
  · simp [← Pi.zero_def]
  by_cases hμm : IsFiniteMeasure μ
  · simp [condVar, hm]
  · simp [condVar, condExp_of_not_integrable, integrable_const_iff_isFiniteMeasure hc,
      integrable_const_iff_isFiniteMeasure <| pow_ne_zero _ hc, hμm, Pi.pow_def]

Goal state after:
Module: Mathlib.Probability.ProductMeasure

Goal state before:
pp: case inl.e_a
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
hab : a ≤ b
s : (i : ↥(Iic b)) → Set (X ↑i)
ms : ∀ (i : ↥(Iic b)), MeasurableSet (s i)
x : ℕ
hx : x ∈ Ioc a b
⊢ Function.extend Subtype.val (fun i ↦ (μ ↑i) (s i)) 1 x =
    Function.extend Subtype.val (fun i ↦ (μ ↑i) (restrict₂ ⋯ s i)) 1 x

Tactic:
rw [Function.extend_val_apply hx, Function.extend_val_apply (Ioc_subset_Iic_self hx),
        restrict₂]

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

Goal state before:
pp: case inl
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), IsMarkovKernel (κ n)
a b : ℕ
hab : a ≤ b
this : IsMarkovKernel ((κ b).map ⇑(piSingleton b))
⊢ (partialTraj κ a (b + 1)).map (frestrictLe₂ ⋯) = partialTraj κ a b

Tactic:
rw [partialTraj_succ_eq_comp hab, map_comp, partialTraj_succ_self, ← map_comp_right,
      frestrictLe₂_comp_IicProdIoc, ← fst_eq, fst_prod, id_comp]

Goal state after:
case inl.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), IsMarkovKernel (κ n)
a b : ℕ
hab : a ≤ b
this : IsMarkovKernel ((κ b).map ⇑(piSingleton b))
⊢ Measurable (_root_.IicProdIoc b (b + 1))

case inl.hg
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), IsMarkovKernel (κ n)
a b : ℕ
hab : a ≤ b
this : IsMarkovKernel ((κ b).map ⇑(piSingleton b))
⊢ Measurable (frestrictLe₂ ⋯)
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
inst✝¹ : IsFiniteKernel κ
inst✝ : IsFiniteKernel η
a : α
h : κ a ≪ η a
⊢ ∫⁻ (c : γ), κ.rnDeriv η a c ∂η a = (κ a) univ

Tactic:
by
  rw [← setLIntegral_univ, setLIntegral_rnDeriv h MeasurableSet.univ]

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

Goal state before:
pp: α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
γ : Type u_3
inst✝³ : MeasurableSpace γ
κ : Kernel α β
η : Kernel β γ
inst✝² : IsMarkovKernel κ
inst✝¹ : IsMarkovKernel η
inst✝ : IsDeterministic (η ∘ₖ κ)
a : α
s : Set γ
t : Set β
hs : MeasurableSet s
ht : MeasurableSet t
this : ∀ (b : β), (η b) s * t.indicator 1 b = t.indicator (fun b ↦ (η b) s) b
h₁ : ((η ∘ₖ κ) a) s = 1
integral_le_kernel : ∫⁻ (b : β) in t, (η b) s ∂κ a ≤ (κ a) t
⊢ ((η ∘ₖ κ) a) sᶜ = 0

Tactic:
by
      rw [measure_compl hs (by simp), measure_univ h₁, h₁, tsub_self]

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

Goal state before:

Tactic:
_ = ∫⁻ b in t, 1 ∂κ a - ∫⁻ b in t, (η b) s ∂κ a := by
      rw [setLIntegral_one]
    _ = ∫⁻ b in t, 1 - (η b) s ∂κ a := by
      rw [lintegral_sub]
      · exact η.measurable_coe hs
      · exact ne_top_of_le_ne_top (by simp) integral_le_kernel
      · refine ae_of_all _ fun b ↦ ?_
        rw [← measure_univ (μ := η b)]
        exact measure_mono (by simp)
    _ ≤ ∫⁻ b, 1 - (η b) s ∂κ a := setLIntegral_le_lintegral _ _
    _ = ∫⁻ x, (η x) sᶜ ∂κ a := by
        congr with x
        rw [measure_compl hs (by simp)]
        simp
    _ = (η ∘ₖ κ) a sᶜ := by
        rw [η.comp_apply' _ _ hs.compl]
    _ = 0 := by
      rw [measure_compl hs (by simp), measure_univ h₁, h₁, tsub_self]

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

Goal state before:
pp: 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∞
hl : Measurable (uncurry ℓ)
μ : Measure 𝓧
inst✝¹ : SFinite μ
π : Measure Θ
inst✝ : SFinite π
hl_pos : μ Set.univ = ∞ → ⨅ y, ∫⁻ (θ : Θ), ℓ θ y ∂π = 0 → ∃ y, ∫⁻ (θ : Θ), ℓ θ y ∂π = 0
h_zero : μ = 0 → Nonempty 𝓨
⊢ ⨅ y, ∫⁻ (θ : Θ), ℓ θ y * μ Set.univ ∂π ≤ ⨅ κ, ⨅ (_ : IsMarkovKernel κ), avgRisk ℓ (Kernel.const Θ μ) κ π

Tactic:
le_iInf_iff

Goal state after:
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∞
hl : Measurable (uncurry ℓ)
μ : Measure 𝓧
inst✝¹ : SFinite μ
π : Measure Θ
inst✝ : SFinite π
hl_pos : μ Set.univ = ∞ → ⨅ y, ∫⁻ (θ : Θ), ℓ θ y ∂π = 0 → ∃ y, ∫⁻ (θ : Θ), ℓ θ y ∂π = 0
h_zero : μ = 0 → Nonempty 𝓨
⊢ ∀ (i : Kernel 𝓧 𝓨), IsMarkovKernel i → ⨅ y, ∫⁻ (θ : Θ), ℓ θ y * μ Set.univ ∂π ≤ avgRisk ℓ (Kernel.const Θ μ) i π
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✝ : DecidableEq ι
s : Finset ι
f : ((i : ι) → X i) → ℝ≥0∞
mf : Measurable f
x : (i : ι) → X i
g : ((i : ↥s) → X ↑i) → ℝ≥0∞ := fun y ↦ f (Function.updateFinset x s y)
this : ∀ (y : (i : ι) → X i), g (s.restrict y) = f y
⊢ ∫⁻ (a : (i : ι) → X i), g (s.restrict a) ∂infinitePi μ = (∫⋯∫⁻_s, f ∂μ) x

Tactic:
lintegral_restrict_infinitePi

Goal state after:
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✝ : DecidableEq ι
s : Finset ι
f : ((i : ι) → X i) → ℝ≥0∞
mf : Measurable f
x : (i : ι) → X i
g : ((i : ↥s) → X ↑i) → ℝ≥0∞ := fun y ↦ f (Function.updateFinset x s y)
this : ∀ (y : (i : ι) → X i), g (s.restrict y) = f y
⊢ (∫⁻ (y : (i : ↥s) → X ↑i), g y ∂Measure.pi fun i ↦ μ ↑i) = (∫⋯∫⁻_s, f ∂μ) x

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✝ : DecidableEq ι
s : Finset ι
f : ((i : ι) → X i) → ℝ≥0∞
mf : Measurable f
x : (i : ι) → X i
g : ((i : ↥s) → X ↑i) → ℝ≥0∞ := fun y ↦ f (Function.updateFinset x s y)
this : ∀ (y : (i : ι) → X i), g (s.restrict y) = f y
⊢ Measurable g
Module: Mathlib.Probability.Decision.Risk.Countable

Goal state before:
pp: Θ : Type u_1
𝓧 : Type u_3
𝓨 : Type u_5
mΘ : MeasurableSpace Θ
m𝓧 : MeasurableSpace 𝓧
m𝓨 : MeasurableSpace 𝓨
ℓ : Θ → 𝓨 → ℝ≥0∞
P : Kernel Θ 𝓧
κ : Kernel 𝓧 𝓨
π : Measure Θ
inst✝¹ : Fintype Θ
inst✝ : MeasurableSingletonClass Θ
⊢ avgRisk ℓ P κ π = ∑ θ, (∫⁻ (y : 𝓨), ℓ θ y ∂(κ ∘ₖ P) θ) * π {θ}

Tactic:
by
  simp [avgRisk, lintegral_fintype]

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

Goal state before:
pp: Θ : Type u_1
𝓧 : Type u_3
𝓨 : Type u_5
mΘ : MeasurableSpace Θ
m𝓧 : MeasurableSpace 𝓧
m𝓨 : MeasurableSpace 𝓨
ℓ : Θ → 𝓨 → ℝ≥0∞
P : Kernel Θ 𝓧
π : Measure Θ
inst✝¹ : Fintype 𝓨
inst✝ : MeasurableSingletonClass 𝓨
hℓ : Measurable ℓ
⊢ bayesRisk ℓ P π = ⨅ κ, ⨅ (_ : IsMarkovKernel κ), ∑ y, ∫⁻ (θ : Θ), ℓ θ y * (⇑κ ∘ₘ P θ) {y} ∂π

Tactic:
by
  simp [bayesRisk, avgRisk_fintype' hℓ]

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

Goal state before:
pp: X : Type u_1
Y : Type u_2
inst✝³ : MeasurableSpace X
inst✝² : MeasurableSpace Y
inst✝¹ : MeasurableSingletonClass X
inst✝ : MeasurableSingletonClass Y
x y : X
f : X → Y
p : ↑I
⊢ map f Ber(x, y, p) = Ber(f x, f y, p)

Tactic:
have hf (x : X) : AEMeasurable f (dirac x) := by fun_prop

Goal state after:
X : Type u_1
Y : Type u_2
inst✝³ : MeasurableSpace X
inst✝² : MeasurableSpace Y
inst✝¹ : MeasurableSingletonClass X
inst✝ : MeasurableSingletonClass Y
x y : X
f : X → Y
p : ↑I
hf : ∀ (x : X), AEMeasurable f (dirac x)
⊢ map f Ber(x, y, p) = Ber(f x, f y, p)
Module: Mathlib.Probability.Distributions.Cauchy

Goal state before:
pp: case neg
x₀ : ℝ
γ : ℝ≥0
h : γ ≠ 0
⊢ Integrable (cauchyPDFReal x₀ γ) volume

Tactic:
apply Integrable.of_integral_ne_zero

Goal state after:
case neg
x₀ : ℝ
γ : ℝ≥0
h : γ ≠ 0
⊢ ∫ (a : ℝ), cauchyPDFReal x₀ γ a ≠ 0
Module: Mathlib.Probability.Distributions.Exponential

Goal state before:
pp: r x : ℝ
⊢ ENNReal.ofReal (if 0 ≤ x then r ^ 1 / Gamma 1 * x ^ (1 - 1) * rexp (-(r * x)) else 0) =
    ENNReal.ofReal (if 0 ≤ x then r * rexp (-(r * x)) else 0)

Tactic:
simp only [rpow_one, Gamma_one, div_one, sub_self, rpow_zero, mul_one]

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

Goal state before:
pp: E : Type u_1
inst✝ : MeasurableSpace E
μ : Measure E
Ω : Type u_2
x✝ : MeasurableSpace Ω
ℙ : Measure Ω
X : Ω → E
s : Set E
⊢ IsUniform X (toMeasurable μ s) ℙ μ ↔ IsUniform X s ℙ μ

Tactic:
unfold IsUniform

Goal state after:
E : Type u_1
inst✝ : MeasurableSpace E
μ : Measure E
Ω : Type u_2
x✝ : MeasurableSpace Ω
ℙ : Measure Ω
X : Ω → E
s : Set E
⊢ Measure.map X ℙ = μ[|toMeasurable μ s] ↔ Measure.map X ℙ = μ[|s]
Module: Mathlib.Probability.Kernel.Condexp

Goal state before:
pp: case inr
Ω : Type u_1
m mΩ : MeasurableSpace Ω
inst✝¹ : StandardBorelSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
hm : m ≤ mΩ
h : Nonempty Ω
⊢ μ.trim hm ⊗ₘ condExpKernel μ m = Measure.map Function.diag μ

Tactic:
condExpKernel_eq,

Goal state after:
case inr
Ω : Type u_1
m mΩ : MeasurableSpace Ω
inst✝¹ : StandardBorelSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
hm : m ≤ mΩ
h : Nonempty Ω
⊢ μ.trim hm ⊗ₘ (condDistrib id id μ).comap id ⋯ = Measure.map Function.diag μ
Module: Mathlib.Probability.Kernel.Condexp

Goal state before:
pp: case inl
Ω : Type u_1
m mΩ : MeasurableSpace Ω
inst✝¹ : StandardBorelSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
s : Set Ω
hs : MeasurableSet s
h : IsEmpty Ω
⊢ (fun ω ↦ ((condExpKernel μ m) ω).real s) =ᵐ[μ] μ[s.indicator fun ω ↦ 1 | m ⊓ mΩ]

Tactic:
have : μ = 0 := Measure.eq_zero_of_isEmpty μ

Goal state after:
case inl
Ω : Type u_1
m mΩ : MeasurableSpace Ω
inst✝¹ : StandardBorelSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
s : Set Ω
hs : MeasurableSet s
h : IsEmpty Ω
this : μ = 0
⊢ (fun ω ↦ ((condExpKernel μ m) ω).real s) =ᵐ[μ] μ[s.indicator fun ω ↦ 1 | m ⊓ mΩ]
Module: Mathlib.Probability.Kernel.Condexp

Goal state before:
pp: case inl
Ω : Type u_1
F : Type u_2
m mΩ : MeasurableSpace Ω
inst✝⁴ : StandardBorelSpace Ω
μ : Measure Ω
inst✝³ : IsFiniteMeasure μ
inst✝² : NormedAddCommGroup F
f : Ω → F
inst✝¹ : NormedSpace ℝ F
inst✝ : CompleteSpace F
hf_int : Integrable f μ
h : IsEmpty Ω
⊢ μ[f | m ⊓ mΩ] =ᵐ[μ] fun ω ↦ ∫ (y : Ω), f y ∂(condExpKernel μ m) ω

Tactic:
have : μ = 0 := Measure.eq_zero_of_isEmpty μ

Goal state after:
case inl
Ω : Type u_1
F : Type u_2
m mΩ : MeasurableSpace Ω
inst✝⁴ : StandardBorelSpace Ω
μ : Measure Ω
inst✝³ : IsFiniteMeasure μ
inst✝² : NormedAddCommGroup F
f : Ω → F
inst✝¹ : NormedSpace ℝ F
inst✝ : CompleteSpace F
hf_int : Integrable f μ
h : IsEmpty Ω
this : μ = 0
⊢ μ[f | m ⊓ mΩ] =ᵐ[μ] fun ω ↦ ∫ (y : Ω), f y ∂(condExpKernel μ m) ω
Module: Mathlib.Probability.Independence.ZeroOne

Goal state before:
pp: α : Type u_1
Ω : Type u_2
ι : Type u_3
_mα : MeasurableSpace α
s : ι → MeasurableSpace Ω
m0 : MeasurableSpace Ω
κ : Kernel α Ω
μα : Measure α
β : Type u_4
p : Set ι → Prop
f : Filter ι
ns : β → Set ι
h_le : ∀ (n : ι), s n ≤ m0
h_indep : iIndep s κ μα
hf : ∀ (t : Set ι), p t → tᶜ ∈ f
hns : Directed (fun x1 x2 ↦ x1 ⊆ x2) ns
hnsp : ∀ (a : β), p (ns a)
hns_univ : ∀ (n : ι), ∃ a, n ∈ ns a
this : ⨆ a, ⨆ n ∈ ns a, s n = ⨆ n, s n
⊢ Indep (⨆ n, s n) (limsup s f) κ μα

Tactic:
rw [← this]
    exact indep_iSup_directed_limsup h_le h_indep hf hns hnsp

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

Goal state before:
pp: X✝ Y✝ : Stoch
κ✝ : X✝ ⟶ Y✝
X Y : Stoch
κ : Kernel X.obj.carrier Y.obj.carrier
inst✝¹ : IsDeterministic κ
inst✝ : IsMarkovKernel κ
⊢ { hom := κ, property := ⋯ } ≫ Δ =
    Δ ≫ { hom := κ ∥ₖ Kernel.id, property := ⋯ } ≫ { hom := Kernel.id ∥ₖ κ, property := ⋯ }

Tactic:
exact Deterministic.copy_natural _

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

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
P : Measure Ω
G : Type u_6
H : Type u_7
inst✝⁸ : TopologicalSpace G
inst✝⁷ : MeasurableSpace G
inst✝⁶ : BorelSpace G
inst✝⁵ : HasOuterApproxClosed G
inst✝⁴ : TopologicalSpace H
inst✝³ : MeasurableSpace H
inst✝² : BorelSpace H
inst✝¹ : HasOuterApproxClosed H
Z : Ω → G
U : Ω → H
inst✝ : IsFiniteMeasure P
mZ : AEMeasurable Z P
mU : AEMeasurable U P
h :
  ∀ (f : G →ᵇ ℝ) (g : H →ᵇ ℝ), ∫ (x : Ω), (⇑f ∘ Z * ⇑g ∘ U) x ∂P = (∫ (x : Ω), (⇑f ∘ Z) x ∂P) * ∫ (x : Ω), (⇑g ∘ U) x ∂P
f : G →ᵇ ℝ
g : H →ᵇ ℝ
⊢ ∫ (p : G × H), f p.1 * g p.2 ∂map (fun ω ↦ (Z ω, U ω)) P = (∫ (z : G), f z ∂map Z P) * ∫ (t : H), g t ∂map U P

Tactic:
integral_map,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
P : Measure Ω
G : Type u_6
H : Type u_7
inst✝⁸ : TopologicalSpace G
inst✝⁷ : MeasurableSpace G
inst✝⁶ : BorelSpace G
inst✝⁵ : HasOuterApproxClosed G
inst✝⁴ : TopologicalSpace H
inst✝³ : MeasurableSpace H
inst✝² : BorelSpace H
inst✝¹ : HasOuterApproxClosed H
Z : Ω → G
U : Ω → H
inst✝ : IsFiniteMeasure P
mZ : AEMeasurable Z P
mU : AEMeasurable U P
h :
  ∀ (f : G →ᵇ ℝ) (g : H →ᵇ ℝ), ∫ (x : Ω), (⇑f ∘ Z * ⇑g ∘ U) x ∂P = (∫ (x : Ω), (⇑f ∘ Z) x ∂P) * ∫ (x : Ω), (⇑g ∘ U) x ∂P
f : G →ᵇ ℝ
g : H →ᵇ ℝ
⊢ ∫ (x : Ω), f (Z x, U x).1 * g (Z x, U x).2 ∂P = (∫ (z : G), f z ∂map Z P) * ∫ (t : H), g t ∂map U P

case hφ
Ω : Type u_1
mΩ : MeasurableSpace Ω
P : Measure Ω
G : Type u_6
H : Type u_7
inst✝⁸ : TopologicalSpace G
inst✝⁷ : MeasurableSpace G
inst✝⁶ : BorelSpace G
inst✝⁵ : HasOuterApproxClosed G
inst✝⁴ : TopologicalSpace H
inst✝³ : MeasurableSpace H
inst✝² : BorelSpace H
inst✝¹ : HasOuterApproxClosed H
Z : Ω → G
U : Ω → H
inst✝ : IsFiniteMeasure P
mZ : AEMeasurable Z P
mU : AEMeasurable U P
h :
  ∀ (f : G →ᵇ ℝ) (g : H →ᵇ ℝ), ∫ (x : Ω), (⇑f ∘ Z * ⇑g ∘ U) x ∂P = (∫ (x : Ω), (⇑f ∘ Z) x ∂P) * ∫ (x : Ω), (⇑g ∘ U) x ∂P
f : G →ᵇ ℝ
g : H →ᵇ ℝ
⊢ AEMeasurable (fun ω ↦ (Z ω, U ω)) P

case hfm
Ω : Type u_1
mΩ : MeasurableSpace Ω
P : Measure Ω
G : Type u_6
H : Type u_7
inst✝⁸ : TopologicalSpace G
inst✝⁷ : MeasurableSpace G
inst✝⁶ : BorelSpace G
inst✝⁵ : HasOuterApproxClosed G
inst✝⁴ : TopologicalSpace H
inst✝³ : MeasurableSpace H
inst✝² : BorelSpace H
inst✝¹ : HasOuterApproxClosed H
Z : Ω → G
U : Ω → H
inst✝ : IsFiniteMeasure P
mZ : AEMeasurable Z P
mU : AEMeasurable U P
h :
  ∀ (f : G →ᵇ ℝ) (g : H →ᵇ ℝ), ∫ (x : Ω), (⇑f ∘ Z * ⇑g ∘ U) x ∂P = (∫ (x : Ω), (⇑f ∘ Z) x ∂P) * ∫ (x : Ω), (⇑g ∘ U) x ∂P
f : G →ᵇ ℝ
g : H →ᵇ ℝ
⊢ AEStronglyMeasurable (fun p ↦ f p.1 * g p.2) (map (fun ω ↦ (Z ω, U ω)) P)