mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: case inr
Ω : Type u_1
inst✝² : MeasurableSpace Ω
ι : Type u_2
L : Filter ι
μ : Measure Ω
μs : ι → Measure Ω
inst✝¹ : IsProbabilityMeasure μ
inst✝ : ∀ (i : ι), IsProbabilityMeasure (μs i)
E : Set Ω
E_mble : MeasurableSet E
h : limsup (fun i ↦ (μs i) E) L ≤ μ E
hne : L.NeBot
meas_Ec : μ Eᶜ = 1 - μ E
⊢ μ Eᶜ ≤ liminf (fun i ↦ (μs i) Eᶜ) L

Tactic:
have meas_i_Ec : ∀ i, μs i Eᶜ = 1 - μs i E := by
    intro i
    simpa only [measure_univ] using measure_compl E_mble (measure_lt_top (μs i) E).ne

Goal state after:
case inr
Ω : Type u_1
inst✝² : MeasurableSpace Ω
ι : Type u_2
L : Filter ι
μ : Measure Ω
μs : ι → Measure Ω
inst✝¹ : IsProbabilityMeasure μ
inst✝ : ∀ (i : ι), IsProbabilityMeasure (μs i)
E : Set Ω
E_mble : MeasurableSet E
h : limsup (fun i ↦ (μs i) E) L ≤ μ E
hne : L.NeBot
meas_Ec : μ Eᶜ = 1 - μ E
meas_i_Ec : ∀ (i : ι), (μs i) Eᶜ = 1 - (μs i) E
⊢ μ Eᶜ ≤ liminf (fun i ↦ (μs i) Eᶜ) L
Module: Mathlib.MeasureTheory.Measure.FiniteMeasure

Goal state before:
pp: case refine_1
Ω : Type u_1
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
γ : Type u_2
𝕜 : Type u_3
inst✝ : RCLike 𝕜
F : Filter γ
μs : γ → FiniteMeasure Ω
μ : FiniteMeasure Ω
h : ∀ (f : Ω →ᵇ ℝ), Tendsto (fun i ↦ ∫ (x : Ω), f x ∂↑(μs i)) F (𝓝 (∫ (x : Ω), f x ∂↑μ))
f : Ω →ᵇ 𝕜
⊢ Tendsto (fun i ↦ ∫ (ω : Ω), f ω ∂↑(μs i)) F (𝓝 (∫ (ω : Ω), f ω ∂↑μ))

pp: case refine_2
Ω : Type u_1
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
γ : Type u_2
𝕜 : Type u_3
inst✝ : RCLike 𝕜
F : Filter γ
μs : γ → FiniteMeasure Ω
μ : FiniteMeasure Ω
h : ∀ (f : Ω →ᵇ 𝕜), Tendsto (fun i ↦ ∫ (ω : Ω), f ω ∂↑(μs i)) F (𝓝 (∫ (ω : Ω), f ω ∂↑μ))
f : Ω →ᵇ ℝ
⊢ Tendsto (fun i ↦ ∫ (x : Ω), f x ∂↑(μs i)) F (𝓝 (∫ (x : Ω), f x ∂↑μ))

Tactic:
· rw [← integral_re_add_im (integrable μ f)]
    simp_rw [← integral_re_add_im (integrable (μs _) f)]
    refine Tendsto.add ?_ ?_
    · exact (RCLike.continuous_ofReal.tendsto _).comp (h (f.comp RCLike.re RCLike.lipschitzWith_re))
    · exact (Tendsto.comp (RCLike.continuous_ofReal.tendsto _)
        (h (f.comp RCLike.im RCLike.lipschitzWith_im))).mul_const _

Goal state after:
case refine_2
Ω : Type u_1
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
γ : Type u_2
𝕜 : Type u_3
inst✝ : RCLike 𝕜
F : Filter γ
μs : γ → FiniteMeasure Ω
μ : FiniteMeasure Ω
h : ∀ (f : Ω →ᵇ 𝕜), Tendsto (fun i ↦ ∫ (ω : Ω), f ω ∂↑(μs i)) F (𝓝 (∫ (ω : Ω), f ω ∂↑μ))
f : Ω →ᵇ ℝ
⊢ Tendsto (fun i ↦ ∫ (x : Ω), f x ∂↑(μs i)) F (𝓝 (∫ (x : Ω), f x ∂↑μ))
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: case inr
Ω : Type u_1
inst✝² : MeasurableSpace Ω
ι : Type u_2
L : Filter ι
μ : Measure Ω
μs : ι → Measure Ω
inst✝¹ : IsProbabilityMeasure μ
inst✝ : ∀ (i : ι), IsProbabilityMeasure (μs i)
E : Set Ω
E_mble : MeasurableSet E
h : μ E ≤ liminf (fun i ↦ (μs i) E) L
hne : L.NeBot
meas_Ec : μ Eᶜ = 1 - μ E
⊢ limsup (fun i ↦ (μs i) Eᶜ) L ≤ μ Eᶜ

Tactic:
have meas_i_Ec : ∀ i, μs i Eᶜ = 1 - μs i E := by
    intro i
    simpa only [measure_univ] using measure_compl E_mble (measure_lt_top (μs i) E).ne

Goal state after:
case inr
Ω : Type u_1
inst✝² : MeasurableSpace Ω
ι : Type u_2
L : Filter ι
μ : Measure Ω
μs : ι → Measure Ω
inst✝¹ : IsProbabilityMeasure μ
inst✝ : ∀ (i : ι), IsProbabilityMeasure (μs i)
E : Set Ω
E_mble : MeasurableSet E
h : μ E ≤ liminf (fun i ↦ (μs i) E) L
hne : L.NeBot
meas_Ec : μ Eᶜ = 1 - μ E
meas_i_Ec : ∀ (i : ι), (μs i) Eᶜ = 1 - (μs i) E
⊢ limsup (fun i ↦ (μs i) Eᶜ) L ≤ μ Eᶜ
Module: Mathlib.MeasureTheory.Measure.FiniteMeasure

Goal state before:
pp: Ω : Type u_1
Ω' : Type u_2
inst✝⁶ : MeasurableSpace Ω
inst✝⁵ : MeasurableSpace Ω'
inst✝⁴ : TopologicalSpace Ω
inst✝³ : TopologicalSpace Ω'
inst✝² : BorelSpace Ω
inst✝¹ : BorelSpace Ω'
inst✝ : NormalSpace Ω'
f : Ω → Ω'
hf : IsClosedEmbedding f
μ : FiniteMeasure Ω'
hμ : μ ∈ {μ | μ (range f)ᶜ = 0}
g : Ω →ᵇ ℝ
g' : Ω' →ᵇ ℝ
hg' : ⇑g' ∘ f = ⇑g
A : ∀ (x : Ω), g x = g' (f x)
B : ∀ {ν : FiniteMeasure Ω'}, ν (range f)ᶜ = 0 → ∫ (y : Ω') in range f, g' y ∂↑ν = ∫ (y : Ω'), g' y ∂↑ν
⊢ Tendsto (fun i ↦ ∫ (y : Ω') in range f, g' y ∂↑i) (𝓝[{μ | μ (range f)ᶜ = 0}] μ) (𝓝 (∫ (y : Ω'), g' y ∂↑μ))

Tactic:
have : Tendsto (fun (ν : FiniteMeasure Ω') ↦ ∫ y, g' y ∂ν) (𝓝[{μ | μ (range f)ᶜ = 0}] μ)
      (𝓝 (∫ (y : Ω'), g' y ∂μ)) := by
    rw [nhdsWithin]
    have A : Tendsto (fun (ν : FiniteMeasure Ω') ↦ ∫ y, g' y ∂ν) (𝓝 μ) (𝓝 (∫ (y : Ω'), g' y ∂μ)) :=
      tendsto_iff_forall_integral_tendsto.1 tendsto_id _
    exact Tendsto.mono_left A inf_le_left

Goal state after:
Ω : Type u_1
Ω' : Type u_2
inst✝⁶ : MeasurableSpace Ω
inst✝⁵ : MeasurableSpace Ω'
inst✝⁴ : TopologicalSpace Ω
inst✝³ : TopologicalSpace Ω'
inst✝² : BorelSpace Ω
inst✝¹ : BorelSpace Ω'
inst✝ : NormalSpace Ω'
f : Ω → Ω'
hf : IsClosedEmbedding f
μ : FiniteMeasure Ω'
hμ : μ ∈ {μ | μ (range f)ᶜ = 0}
g : Ω →ᵇ ℝ
g' : Ω' →ᵇ ℝ
hg' : ⇑g' ∘ f = ⇑g
A : ∀ (x : Ω), g x = g' (f x)
B : ∀ {ν : FiniteMeasure Ω'}, ν (range f)ᶜ = 0 → ∫ (y : Ω') in range f, g' y ∂↑ν = ∫ (y : Ω'), g' y ∂↑ν
this : Tendsto (fun ν ↦ ∫ (y : Ω'), g' y ∂↑ν) (𝓝[{μ | μ (range f)ᶜ = 0}] μ) (𝓝 (∫ (y : Ω'), g' y ∂↑μ))
⊢ Tendsto (fun i ↦ ∫ (y : Ω') in range f, g' y ∂↑i) (𝓝[{μ | μ (range f)ᶜ = 0}] μ) (𝓝 (∫ (y : Ω'), g' y ∂↑μ))
Module: Mathlib.Probability.Independence.Kernel.Indep

Goal state before:
pp: α : Type u_1
Ω : Type u_2
ι : Type u_3
_mα : MeasurableSpace α
_mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
π : ι → Set (Set Ω)
ι' : Type u_5
g : ι' → ι
hg : Function.Injective g
h : iIndepSets π κ μ
s : Finset ι'
⊢ ∀ {f : ι' → Set Ω}, (∀ i ∈ s, f i ∈ (π ∘ g) i) → ∀ᵐ (a : α) ∂μ, (κ a) (⋂ i ∈ s, f i) = ∏ i ∈ s, (κ a) (f i)

Tactic:
f

Goal state after:
α : Type u_1
Ω : Type u_2
ι : Type u_3
_mα : MeasurableSpace α
_mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
π : ι → Set (Set Ω)
ι' : Type u_5
g : ι' → ι
hg : Function.Injective g
h : iIndepSets π κ μ
s : Finset ι'
f : ι' → Set Ω
⊢ (∀ i ∈ s, f i ∈ (π ∘ g) i) → ∀ᵐ (a : α) ∂μ, (κ a) (⋂ i ∈ s, f i) = ∏ i ∈ s, (κ a) (f i)
Module: Mathlib.MeasureTheory.Measure.FiniteMeasure

Goal state before:
pp: Ω' : Type u_2
inst✝⁶ : MeasurableSpace Ω'
inst✝⁵ : TopologicalSpace Ω'
inst✝⁴ : BorelSpace Ω'
Ω : Type u_3
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : BorelSpace Ω
inst✝ : NormalSpace Ω'
f : Ω → Ω'
hf : IsClosedEmbedding f
M : Set (FiniteMeasure Ω') := {μ | μ (range f)ᶜ = 0}
A : IsEmbedding Subtype.val
μ : ↑M
⊢ (fun μ ↦ ⟨μ.map f, ⋯⟩) (M.domRestrict (fun μ ↦ comap f μ) μ) = μ

Tactic:
ext s hs
      simp only [Set.domRestrict_apply, toMeasure_map]
      rw [Measure.map_apply hf.continuous.measurable hs]
      simp only [toMeasure_comap]
      rw [Measure.comap_apply _ hf.injective, image_preimage_eq_inter_range]
      · rw [← Measure.restrict_apply hs, Measure.restrict_eq_self_of_ae_mem]
        exact (null_iff_toMeasure_null (↑μ) (range f)ᶜ).mp (by exact μ.2)
      · exact fun t ht ↦ hf.measurableEmbedding.measurableSet_image' ht
      · exact hf.continuous.measurable hs

Goal state after:
Module: Mathlib.Probability.Independence.Kernel.Indep

Goal state before:
pp: α : Type u_1
Ω : Type u_2
_mα : MeasurableSpace α
m1 m2 m : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
inst✝ : IsZeroOrMarkovKernel κ
p1 p2 : Set (Set Ω)
h1 : m1 ≤ m
h2 : m2 ≤ m
hp1 : IsPiSystem p1
hp2 : IsPiSystem p2
hpm1 : m1 = generateFrom p1
hpm2 : m2 = generateFrom p2
hyp : IndepSets p1 p2 κ μ
h : IsMarkovKernel κ
t1 t2 : Set Ω
ht2 : t2 ∈ {s | MeasurableSet s}
t : Set Ω
ht : MeasurableSet t
iht : ∀ᵐ (a : α) ∂μ, (κ a) (t ∩ t2) = (κ a) t * (κ a) t2
a : α
ha : (κ a) (t ∩ t2) = (κ a) t * (κ a) t2
⊢ tᶜ ∩ t2 = t2 \ (t ∩ t2)

Tactic:
Set.inter_comm t,

Goal state after:
α : Type u_1
Ω : Type u_2
_mα : MeasurableSpace α
m1 m2 m : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
inst✝ : IsZeroOrMarkovKernel κ
p1 p2 : Set (Set Ω)
h1 : m1 ≤ m
h2 : m2 ≤ m
hp1 : IsPiSystem p1
hp2 : IsPiSystem p2
hpm1 : m1 = generateFrom p1
hpm2 : m2 = generateFrom p2
hyp : IndepSets p1 p2 κ μ
h : IsMarkovKernel κ
t1 t2 : Set Ω
ht2 : t2 ∈ {s | MeasurableSet s}
t : Set Ω
ht : MeasurableSet t
iht : ∀ᵐ (a : α) ∂μ, (κ a) (t ∩ t2) = (κ a) t * (κ a) t2
a : α
ha : (κ a) (t ∩ t2) = (κ a) t * (κ a) t2
⊢ tᶜ ∩ t2 = t2 \ (t2 ∩ t)
Module: Mathlib.Probability.Independence.Basic

Goal state before:
pp: Ω : Type u_1
ι : Type u_2
_mΩ : MeasurableSpace Ω
μ : Measure Ω
β : ι → Type u_10
m : (i : ι) → MeasurableSpace (β i)
f : (i : ι) → Ω → β i
h : ∀ (s : Finset ι), iIndepFun (s.restrict f) μ
s : Finset ι
⊢ ∀ {f' : ι → Set Ω}, (∀ i ∈ s, MeasurableSet (f' i)) → μ (⋂ i ∈ s, f' i) = ∏ i ∈ s, μ (f' i)

Tactic:
f

Goal state after:
Ω : Type u_1
ι : Type u_2
_mΩ : MeasurableSpace Ω
μ : Measure Ω
β : ι → Type u_10
m : (i : ι) → MeasurableSpace (β i)
f✝ : (i : ι) → Ω → β i
h : ∀ (s : Finset ι), iIndepFun (s.restrict f✝) μ
s : Finset ι
f : ι → Set Ω
⊢ (∀ i ∈ s, MeasurableSet (f i)) → μ (⋂ i ∈ s, f i) = ∏ i ∈ s, μ (f i)
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: case mp
γ : Type u_1
Ω : Type u_2
mΩ : MeasurableSpace Ω
inst✝² : PseudoEMetricSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
F : Filter γ
inst✝ : F.IsCountablyGenerated
μs : γ → ProbabilityMeasure Ω
μ : ProbabilityMeasure Ω
h : Tendsto μs F (𝓝 μ)
⊢ ∀ (f : Ω → ℝ),
    (∃ C, ∀ (x y : Ω), dist (f x) (f y) ≤ C) →
      (∃ L, LipschitzWith L f) → Tendsto (fun i ↦ ∫ (ω : Ω), f ω ∂↑(μs i)) F (𝓝 (∫ (ω : Ω), f ω ∂↑μ))

Tactic:
f

Goal state after:
case mp
γ : Type u_1
Ω : Type u_2
mΩ : MeasurableSpace Ω
inst✝² : PseudoEMetricSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
F : Filter γ
inst✝ : F.IsCountablyGenerated
μs : γ → ProbabilityMeasure Ω
μ : ProbabilityMeasure Ω
h : Tendsto μs F (𝓝 μ)
f : Ω → ℝ
⊢ (∃ C, ∀ (x y : Ω), dist (f x) (f y) ≤ C) →
    (∃ L, LipschitzWith L f) → Tendsto (fun i ↦ ∫ (ω : Ω), f ω ∂↑(μs i)) F (𝓝 (∫ (ω : Ω), f ω ∂↑μ))
Module: Mathlib.Probability.Independence.Basic

Goal state before:

Tactic:
_ = (μ (⋂ i, Y i ⁻¹' t i))⁻¹ * μ ((⋂ i, Y i ⁻¹' t i) ∩ ⋂ i ∈ s, f i) := by
      rw [cond_apply]; exact .iInter fun i ↦ hY i (ht i)
    _ = (μ (⋂ i, Y i ⁻¹' t i))⁻¹ * μ (⋂ i, g i) := by
      congr
      calc
        _ = (⋂ i, Y i ⁻¹' t i) ∩ ⋂ i, if i ∈ s then f i else .univ := by
          simp only [Set.iInter_ite, Set.iInter_univ, Set.inter_univ]
        _ = ⋂ i, Y i ⁻¹' t i ∩ (if i ∈ s then f i else .univ) := by rw [Set.iInter_inter_distrib]
        _ = _ := Set.iInter_congr fun i ↦ by by_cases hi : i ∈ s <;> simp [hi, g]
    _ = (∏ i, μ (Y i ⁻¹' t i))⁻¹ * μ (⋂ i, g i) := by
      rw [hindep.meas_iInter]
      exact fun i ↦ ⟨.univ ×ˢ t i, MeasurableSet.univ.prod (ht _), by ext; simp⟩
    _ = (∏ i, μ (Y i ⁻¹' t i))⁻¹ * ∏ i, μ (g i) := by
      rw [hindep.meas_iInter]
      intro i
      by_cases hi : i ∈ s <;> simp only [hi, ↓reduceIte, g]
      · obtain ⟨A, hA, hA'⟩ := hf i hi
        exact .inter ⟨.univ ×ˢ t i, MeasurableSet.univ.prod (ht _), by ext; simp⟩
          ⟨A ×ˢ Set.univ, hA.prod .univ, by ext; simp [← hA']⟩
      · exact ⟨.univ ×ˢ t i, MeasurableSet.univ.prod (ht _), by ext; simp⟩
    _ = ∏ i, (μ (Y i ⁻¹' t i))⁻¹ * μ (g i) := by
      rw [Finset.prod_mul_distrib, ENNReal.prod_inv_distrib]
      exact fun _ _ i _ _ ↦ .inr <| measure_ne_top _ _
    _ = ∏ i, if i ∈ s then μ[f i | Y i ⁻¹' t i] else 1 := by
      refine Finset.prod_congr rfl fun i _ ↦ ?_
      by_cases hi : i ∈ s
      · simp only [hi, ↓reduceIte, g, cond_apply (hY i (ht i))]
      · simp only [hi, ↓reduceIte, g, ENNReal.inv_mul_cancel (hy i hi) (measure_ne_top μ _)]
    _ = _ := by simp

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

Goal state before:
pp: ι : Type u_6
Ω : Type u_7
α : Type u_8
β : Type u_9
mΩ : MeasurableSpace Ω
mα : MeasurableSpace α
mβ : MeasurableSpace β
μ : Measure Ω
X : ι → Ω → α
Y : ι → Ω → β
t : ι → Set β
inst✝ : Finite ι
hY : ∀ (i : ι), Measurable (Y i)
hindep : iIndepFun (fun i ω ↦ (X i ω, Y i ω)) μ
hy : ∀ (i : ι), μ (Y i ⁻¹' t i) ≠ 0
ht : ∀ (i : ι), MeasurableSet (t i)
s : Finset ι
⊢ ∀ {f' : ι → Set Ω},
    (∀ i ∈ s, MeasurableSet (f' i)) → μ[⋂ i ∈ s, f' i | ⋂ i, Y i ⁻¹' t i] = ∏ i ∈ s, μ[f' i | ⋂ i, Y i ⁻¹' t i]

Tactic:
f

Goal state after:
ι : Type u_6
Ω : Type u_7
α : Type u_8
β : Type u_9
mΩ : MeasurableSpace Ω
mα : MeasurableSpace α
mβ : MeasurableSpace β
μ : Measure Ω
X : ι → Ω → α
Y : ι → Ω → β
t : ι → Set β
inst✝ : Finite ι
hY : ∀ (i : ι), Measurable (Y i)
hindep : iIndepFun (fun i ω ↦ (X i ω, Y i ω)) μ
hy : ∀ (i : ι), μ (Y i ⁻¹' t i) ≠ 0
ht : ∀ (i : ι), MeasurableSet (t i)
s : Finset ι
f : ι → Set Ω
⊢ (∀ i ∈ s, MeasurableSet (f i)) → μ[⋂ i ∈ s, f i | ⋂ i, Y i ⁻¹' t i] = ∏ i ∈ s, μ[f i | ⋂ i, Y i ⁻¹' t i]
Module: Mathlib.Probability.Density

Goal state before:
pp: Ω : Type u_1
E : Type u_2
inst✝⁴ : MeasurableSpace E
m : MeasurableSpace Ω
ℙ : Measure Ω
μ : Measure E
F : Type u_3
inst✝³ : NormedAddCommGroup F
inst✝² : NormedSpace ℝ F
inst✝¹ : IsFiniteMeasure ℙ
X : Ω → E
inst✝ : HasPDF X ℙ μ
f : E → F
hf : AEStronglyMeasurable f μ
⊢ Integrable (fun x ↦ (pdf X ℙ μ x).toReal • f x) μ ↔ Integrable (fun x ↦ f (X x)) ℙ

Tactic:
rw [← Function.comp_def,
    ← integrable_map_measure (hf.mono_ac HasPDF.absolutelyContinuous) (HasPDF.aemeasurable X ℙ μ),
    map_eq_withDensity_pdf X ℙ μ, pdf_def, integrable_rnDeriv_smul_iff HasPDF.absolutelyContinuous]

Goal state after:
Ω : Type u_1
E : Type u_2
inst✝⁴ : MeasurableSpace E
m : MeasurableSpace Ω
ℙ : Measure Ω
μ : Measure E
F : Type u_3
inst✝³ : NormedAddCommGroup F
inst✝² : NormedSpace ℝ F
inst✝¹ : IsFiniteMeasure ℙ
X : Ω → E
inst✝ : HasPDF X ℙ μ
f : E → F
hf : AEStronglyMeasurable f μ
⊢ Integrable f (map X ℙ) ↔ Integrable f (μ.withDensity ((map X ℙ).rnDeriv μ))
Module: Mathlib.Probability.Density

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
ℙ : Measure Ω
X : Ω → ℝ
inst✝ : IsFiniteMeasure ℙ
f : ℝ → ℝ
g : ℝ → ℝ≥0∞
hg : pdf X ℙ volume =ᵐ[volume] g
hgi : ∫⁻ (x : ℝ), ‖f x‖ₑ * g x ≠ ∞
⊢ HasFiniteIntegral (fun x ↦ f x * (pdf X ℙ volume x).toReal) volume

Tactic:
rw [hasFiniteIntegral_iff_enorm]
  have : (fun x => ‖f x‖ₑ * g x) =ᵐ[volume] fun x => ‖f x * (pdf X ℙ volume x).toReal‖ₑ := by
    refine ae_eq_trans ((ae_eq_refl _).fun_mul (ae_eq_trans hg.symm ofReal_toReal_ae_eq.symm)) ?_
    simp_rw [← smul_eq_mul, enorm_smul, smul_eq_mul]
    refine .fun_mul (ae_eq_refl _) ?_
    simp only [Real.enorm_eq_ofReal ENNReal.toReal_nonneg, ae_eq_refl]
  rwa [lt_top_iff_ne_top, ← lintegral_congr_ae this]

Goal state after:
Module: Mathlib.Probability.Density

Goal state before:
pp: Ω : Type u_1
G : Type u_2
mΩ : MeasurableSpace Ω
ℙ : Measure Ω
inst✝⁶ : Group G
mG : MeasurableSpace G
inst✝⁵ : MeasurableMul₂ G
inst✝⁴ : MeasurableInv G
μ : Measure G
inst✝³ : μ.IsMulLeftInvariant
X Y : Ω → G
inst✝² : SigmaFinite μ
inst✝¹ : HasPDF X ℙ μ
inst✝ : HasPDF Y ℙ μ
σX : SigmaFinite (map X ℙ)
σY : SigmaFinite (map Y ℙ)
hXY : X ⟂ᵢ[ℙ] Y
⊢ pdf (X * Y) ℙ μ =ᵐ[μ] pdf X ℙ μ ⋆ₘₗ[μ] pdf Y ℙ μ

Tactic:
rw [pdf, hXY.map_mul_eq_map_mconv_map₀' (HasPDF.aemeasurable' μ) (HasPDF.aemeasurable' μ) σX σY]
  apply rnDeriv_mconv' <;> exact HasPDF.absolutelyContinuous

Goal state after:
Module: Mathlib.Probability.Independence.Kernel.Indep

Goal state before:
pp: case inr
α : Type u_1
Ω : Type u_2
ι : Type u_3
_mα : MeasurableSpace α
_mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
m : ι → MeasurableSpace Ω
h_le : ∀ (i : ι), m i ≤ _mΩ
π : ι → Set (Set Ω)
h_pi : ∀ (n : ι), IsPiSystem (π n)
h_generate : ∀ (i : ι), m i = generateFrom (π i)
h_ind : iIndepSets π κ μ
hμ : μ ≠ 0
η : Kernel α Ω
η_eq : ⇑κ =ᵐ[μ] ⇑η
hη : IsMarkovKernel η
s : Finset ι
⊢ ∀ {f : ι → Set Ω},
    (∀ i ∈ s, f i ∈ (fun x ↦ {s | MeasurableSet s}) i) → ∀ᵐ (a : α) ∂μ, (η a) (⋂ i ∈ s, f i) = ∏ i ∈ s, (η a) (f i)

Tactic:
f

Goal state after:
case inr
α : Type u_1
Ω : Type u_2
ι : Type u_3
_mα : MeasurableSpace α
_mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
m : ι → MeasurableSpace Ω
h_le : ∀ (i : ι), m i ≤ _mΩ
π : ι → Set (Set Ω)
h_pi : ∀ (n : ι), IsPiSystem (π n)
h_generate : ∀ (i : ι), m i = generateFrom (π i)
h_ind : iIndepSets π κ μ
hμ : μ ≠ 0
η : Kernel α Ω
η_eq : ⇑κ =ᵐ[μ] ⇑η
hη : IsMarkovKernel η
s : Finset ι
f : ι → Set Ω
⊢ (∀ i ∈ s, f i ∈ (fun x ↦ {s | MeasurableSet s}) i) → ∀ᵐ (a : α) ∂μ, (η a) (⋂ i ∈ s, f i) = ∏ i ∈ s, (η a) (f i)
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: Ω : Type u_1
inst✝² : MeasurableSpace Ω
inst✝¹ : TopologicalSpace Ω
inst✝ : SecondCountableTopology Ω
S : Set (Set Ω)
ν : ProbabilityMeasure Ω
h : ∀ (u : Set Ω), IsOpen[inst✝¹] u → ∀ x ∈ u, ∃ s ∈ S, s ∈ 𝓝 x ∧ s ⊆ u
G : Set Ω
hG : IsOpen[inst✝¹] G
r : ℝ≥0
hr : r < ν G
T : Set (Set Ω)
TS : T ⊆ S
T_count : T.Countable
hT : ⋃ t ∈ T, t = G
this✝ : T.Nonempty
f : ℕ → Set Ω
hf : T = range f
G_eq : G = ⋃ n, f n
this : Tendsto (fun i ↦ ν (accumulate f i)) atTop (𝓝 (ν (⋃ i, f i)))
⊢ ∃ T, (∀ t ∈ T, t ∈ S) ∧ r < ν (⋃ t ∈ T, t) ∧ ⋃ t ∈ T, t ⊆ G

Tactic:
rw [← G_eq] at this

Goal state after:
Ω : Type u_1
inst✝² : MeasurableSpace Ω
inst✝¹ : TopologicalSpace Ω
inst✝ : SecondCountableTopology Ω
S : Set (Set Ω)
ν : ProbabilityMeasure Ω
h : ∀ (u : Set Ω), IsOpen[inst✝¹] u → ∀ x ∈ u, ∃ s ∈ S, s ∈ 𝓝 x ∧ s ⊆ u
G : Set Ω
hG : IsOpen[inst✝¹] G
r : ℝ≥0
hr : r < ν G
T : Set (Set Ω)
TS : T ⊆ S
T_count : T.Countable
hT : ⋃ t ∈ T, t = G
this✝ : T.Nonempty
f : ℕ → Set Ω
hf : T = range f
G_eq : G = ⋃ n, f n
this : Tendsto (fun i ↦ ν (accumulate f i)) atTop (𝓝 (ν G))
⊢ ∃ T, (∀ t ∈ T, t ∈ S) ∧ r < ν (⋃ t ∈ T, t) ∧ ⋃ t ∈ T, t ⊆ G
Module: Mathlib.Probability.Independence.Kernel.Indep

Goal state before:
pp: α : Type u_1
Ω : Type u_2
ι : Type u_3
_mα : MeasurableSpace α
_mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
m : ι → MeasurableSpace Ω
h_le : ∀ (i : ι), m i ≤ _mΩ
π : ι → Set (Set Ω)
h_pi : ∀ (n : ι), IsPiSystem (π n)
h_generate : ∀ (i : ι), m i = generateFrom (π i)
h_ind : iIndepSets π κ μ
hμ : μ ≠ 0
η : Kernel α Ω
η_eq : ⇑κ =ᵐ[μ] ⇑η
hη : IsMarkovKernel η
s : Finset ι
f : ι → Set Ω
a : ι
S : Finset ι
ha_notin_S : a ∉ S
h_rec : (∀ i ∈ S, f i ∈ (fun x ↦ {s | MeasurableSet s}) i) → ∀ᵐ (a : α) ∂μ, (η a) (⋂ i ∈ S, f i) = ∏ i ∈ S, (η a) (f i)
hf_m : ∀ i ∈ insert a S, f i ∈ (fun x ↦ {s | MeasurableSet s}) i
hf_m_S : ∀ x ∈ S, MeasurableSet (f x)
p : Set (Set Ω) := piiUnionInter π ↑S
m_p : MeasurableSpace Ω := generateFrom p
hS_eq_generate : m_p = generateFrom p
⊢ Indep m_p (m a) η μ

Tactic:
have hp : IsPiSystem p := isPiSystem_piiUnionInter π h_pi S

Goal state after:
α : Type u_1
Ω : Type u_2
ι : Type u_3
_mα : MeasurableSpace α
_mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
m : ι → MeasurableSpace Ω
h_le : ∀ (i : ι), m i ≤ _mΩ
π : ι → Set (Set Ω)
h_pi : ∀ (n : ι), IsPiSystem (π n)
h_generate : ∀ (i : ι), m i = generateFrom (π i)
h_ind : iIndepSets π κ μ
hμ : μ ≠ 0
η : Kernel α Ω
η_eq : ⇑κ =ᵐ[μ] ⇑η
hη : IsMarkovKernel η
s : Finset ι
f : ι → Set Ω
a : ι
S : Finset ι
ha_notin_S : a ∉ S
h_rec : (∀ i ∈ S, f i ∈ (fun x ↦ {s | MeasurableSet s}) i) → ∀ᵐ (a : α) ∂μ, (η a) (⋂ i ∈ S, f i) = ∏ i ∈ S, (η a) (f i)
hf_m : ∀ i ∈ insert a S, f i ∈ (fun x ↦ {s | MeasurableSet s}) i
hf_m_S : ∀ x ∈ S, MeasurableSet (f x)
p : Set (Set Ω) := piiUnionInter π ↑S
m_p : MeasurableSpace Ω := generateFrom p
hS_eq_generate : m_p = generateFrom p
hp : IsPiSystem p
⊢ Indep m_p (m a) η μ
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
hX : MemLp X 2 μ
hY : MemLp Y 2 μ
⊢ ∫ (ω : Ω), X ω * (Y ω - ∫ (x : Ω), Y x ∂μ) - (∫ (x : Ω), X x ∂μ) * (Y ω - ∫ (x : Ω), Y x ∂μ) ∂μ =
    ∫ (x : Ω), (X * Y) x ∂μ - (∫ (x : Ω), X x ∂μ) * ∫ (x : Ω), Y x ∂μ

Tactic:
mul_sub

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
hX : MemLp X 2 μ
hY : MemLp Y 2 μ
⊢ ∫ (ω : Ω),
      X ω * Y ω - X ω * ∫ (x : Ω), Y x ∂μ - ((∫ (x : Ω), X x ∂μ) * Y ω - (∫ (x : Ω), X x ∂μ) * ∫ (x : Ω), Y x ∂μ) ∂μ =
    ∫ (x : Ω), (X * Y) x ∂μ - (∫ (x : Ω), X x ∂μ) * ∫ (x : Ω), Y x ∂μ
Module: Mathlib.Probability.Independence.Integration

Goal state before:
pp: case iSup
Ω : Type u_1
Mf mΩ : MeasurableSpace Ω
μ : Measure Ω
hMf : Mf ≤ mΩ
c : ℝ≥0∞
T : Set Ω
h_meas_T : MeasurableSet T
h_ind : IndepSets {s | MeasurableSet s} {T} μ
h_mul_indicator : ∀ (g : Ω → ℝ≥0∞), Measurable g → Measurable fun a ↦ g a * T.indicator (fun x ↦ c) a
f : ℕ → Ω → ℝ≥0∞
h_meas_f : ∀ (n : ℕ), Measurable (f n)
⊢ Monotone f →
    (∀ (n : ℕ),
        ∫⁻ (ω : Ω), f n ω * T.indicator (fun x ↦ c) ω ∂μ =
          (∫⁻ (ω : Ω), f n ω ∂μ) * ∫⁻ (ω : Ω), T.indicator (fun x ↦ c) ω ∂μ) →
      ∫⁻ (ω : Ω), (fun x ↦ ⨆ n, f n x) ω * T.indicator (fun x ↦ c) ω ∂μ =
        (∫⁻ (ω : Ω), (fun x ↦ ⨆ n, f n x) ω ∂μ) * ∫⁻ (ω : Ω), T.indicator (fun x ↦ c) ω ∂μ

Tactic:
h_mono_f

Goal state after:
case iSup
Ω : Type u_1
Mf mΩ : MeasurableSpace Ω
μ : Measure Ω
hMf : Mf ≤ mΩ
c : ℝ≥0∞
T : Set Ω
h_meas_T : MeasurableSet T
h_ind : IndepSets {s | MeasurableSet s} {T} μ
h_mul_indicator : ∀ (g : Ω → ℝ≥0∞), Measurable g → Measurable fun a ↦ g a * T.indicator (fun x ↦ c) a
f : ℕ → Ω → ℝ≥0∞
h_meas_f : ∀ (n : ℕ), Measurable (f n)
h_mono_f : Monotone f
⊢ (∀ (n : ℕ),
      ∫⁻ (ω : Ω), f n ω * T.indicator (fun x ↦ c) ω ∂μ =
        (∫⁻ (ω : Ω), f n ω ∂μ) * ∫⁻ (ω : Ω), T.indicator (fun x ↦ c) ω ∂μ) →
    ∫⁻ (ω : Ω), (fun x ↦ ⨆ n, f n x) ω * T.indicator (fun x ↦ c) ω ∂μ =
      (∫⁻ (ω : Ω), (fun x ↦ ⨆ n, f n x) ω ∂μ) * ∫⁻ (ω : Ω), T.indicator (fun x ↦ c) ω ∂μ
Module: Mathlib.Probability.Moments.Variance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
h : X =ᵐ[μ] Y
⊢ eVar[X; μ] = eVar[Y; μ]

Tactic:
evariance,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
h : X =ᵐ[μ] Y
⊢ ∫⁻ (ω : Ω), ‖X ω - ∫ (x : Ω), X x ∂μ‖ₑ ^ 2 ∂μ = ∫⁻ (ω : Ω), ‖Y ω - ∫ (x : Ω), Y x ∂μ‖ₑ ^ 2 ∂μ
Module: Mathlib.Probability.Moments.Variance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
c : ℝ
X : Ω → ℝ
μ : Measure Ω
⊢ eVar[fun ω ↦ c * X ω; μ] = ENNReal.ofReal (c ^ 2) * eVar[X; μ]

Tactic:
evariance,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
c : ℝ
X : Ω → ℝ
μ : Measure Ω
⊢ ∫⁻ (ω : Ω), ‖c * X ω - ∫ (x : Ω), c * X x ∂μ‖ₑ ^ 2 ∂μ = ENNReal.ofReal (c ^ 2) * eVar[X; μ]
Module: Mathlib.Probability.Moments.Variance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
c : ℝ
X : Ω → ℝ
μ : Measure Ω
⊢ ∫⁻ (ω : Ω), ‖c * X ω - ∫ (x : Ω), c * X x ∂μ‖ₑ ^ 2 ∂μ = ENNReal.ofReal (c ^ 2) * eVar[X; μ]

Tactic:
evariance,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
c : ℝ
X : Ω → ℝ
μ : Measure Ω
⊢ ∫⁻ (ω : Ω), ‖c * X ω - ∫ (x : Ω), c * X x ∂μ‖ₑ ^ 2 ∂μ =
    ENNReal.ofReal (c ^ 2) * ∫⁻ (ω : Ω), ‖X ω - ∫ (x : Ω), X x ∂μ‖ₑ ^ 2 ∂μ
Module: Mathlib.Probability.Moments.Variance

Goal state before:
pp: case pos
Ω : Type u_1
mΩ : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
hX : AEStronglyMeasurable X μ
c : ℝ
hX_Lp : MemLp X 2 μ
hX_int : Integrable X μ
⊢ ∫ (ω : Ω), (X ω + c - (∫ (a : Ω), X a ∂μ + μ.real Set.univ • c)) ^ 2 ∂μ = Var[X; μ]

Tactic:
variance_eq_integral hX.aemeasurable

Goal state after:
case pos
Ω : Type u_1
mΩ : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
hX : AEStronglyMeasurable X μ
c : ℝ
hX_Lp : MemLp X 2 μ
hX_int : Integrable X μ
⊢ ∫ (ω : Ω), (X ω + c - (∫ (a : Ω), X a ∂μ + μ.real Set.univ • c)) ^ 2 ∂μ = ∫ (ω : Ω), (X ω - ∫ (x : Ω), X x ∂μ) ^ 2 ∂μ
Module: Mathlib.Probability.Moments.Variance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
Ω' : Type u_3
mΩ' : MeasurableSpace Ω'
μ : Measure Ω'
X : Ω → ℝ
Y : Ω' ≃ᵐ Ω
⊢ eVar[X; Measure.map (⇑Y) μ].toReal = eVar[X ∘ ⇑Y; μ].toReal

Tactic:
evariance,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
Ω' : Type u_3
mΩ' : MeasurableSpace Ω'
μ : Measure Ω'
X : Ω → ℝ
Y : Ω' ≃ᵐ Ω
⊢ (∫⁻ (ω : Ω), ‖X ω - ∫ (x : Ω), X x ∂Measure.map (⇑Y) μ‖ₑ ^ 2 ∂Measure.map (⇑Y) μ).toReal =
    (∫⁻ (ω : Ω'), ‖(X ∘ ⇑Y) ω - ∫ (x : Ω'), (X ∘ ⇑Y) x ∂μ‖ₑ ^ 2 ∂μ).toReal
Module: Mathlib.Probability.Moments.Variance

Goal state before:
pp: case pos
Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
X : Ω → ℝ
hm : AEStronglyMeasurable X μ
hX : ¬MemLp X 2 μ
hint : Integrable X μ
⊢ ∫ (a : Ω), (X a - ∫ (x : Ω), X x ∂μ) ^ 2 ∂μ ≤ ∫ (x : Ω), (X ^ 2) x ∂μ

Tactic:
rw [integral_undef]
      · exact integral_nonneg fun a => sq_nonneg _
      intro h
      have A : MemLp (X - fun ω : Ω => μ[X]) 2 μ :=
        (memLp_two_iff_integrable_sq (by fun_prop)).2 h
      have B : MemLp (fun _ : Ω => μ[X]) 2 μ := memLp_const _
      apply hX
      convert! A.add B
      simp

Goal state after: