mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.MeasureTheory.Function.UniformIntegrable

Goal state before:
pp: α : Type u_1
β : Type u_2
ι : Type u_3
m : MeasurableSpace α
μ : Measure α
inst✝ : NormedAddCommGroup β
p : ℝ≥0∞
f : ι → α → β
hp : p ≠ 0
hp' : p ≠ ∞
hf : ∀ (i : ι), StronglyMeasurable (f i)
ε : ℝ
hε : 0 < ε
hfu : UnifIntegrable f p μ
M : ℝ≥0
hM : ∀ (i : ι), eLpNorm (f i) p μ ≤ ↑M
ℐ : ℝ≥0 → ι
δ : ℝ≥0
hδpos : 0 < ↑δ
hδ :
  ∀ (i : ι) (s : Set α), MeasurableSet s → μ s ≤ ENNReal.ofReal ↑δ → eLpNorm (s.indicator (f i)) p μ ≤ ENNReal.ofReal ε
hℐ : ∀ (C : ℝ≥0), ENNReal.ofReal ↑δ < μ {x | C ≤ ‖f (ℐ C) x‖₊}
C : ℝ≥0
x : α
hx : x ∈ {x | C ≤ ‖f (ℐ C) x‖₊}
⊢ C ≤ ‖{x | C ≤ ‖f (ℐ C) x‖₊}.indicator (f (ℐ C)) x‖₊

Tactic:
nnnorm_indicator_eq_indicator_nnnorm,

Goal state after:
α : Type u_1
β : Type u_2
ι : Type u_3
m : MeasurableSpace α
μ : Measure α
inst✝ : NormedAddCommGroup β
p : ℝ≥0∞
f : ι → α → β
hp : p ≠ 0
hp' : p ≠ ∞
hf : ∀ (i : ι), StronglyMeasurable (f i)
ε : ℝ
hε : 0 < ε
hfu : UnifIntegrable f p μ
M : ℝ≥0
hM : ∀ (i : ι), eLpNorm (f i) p μ ≤ ↑M
ℐ : ℝ≥0 → ι
δ : ℝ≥0
hδpos : 0 < ↑δ
hδ :
  ∀ (i : ι) (s : Set α), MeasurableSet s → μ s ≤ ENNReal.ofReal ↑δ → eLpNorm (s.indicator (f i)) p μ ≤ ENNReal.ofReal ε
hℐ : ∀ (C : ℝ≥0), ENNReal.ofReal ↑δ < μ {x | C ≤ ‖f (ℐ C) x‖₊}
C : ℝ≥0
x : α
hx : x ∈ {x | C ≤ ‖f (ℐ C) x‖₊}
⊢ C ≤ {x | C ≤ ‖f (ℐ C) x‖₊}.indicator (fun a ↦ ‖f (ℐ C) a‖₊) x
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 : FiniteMeasure Ω
⊢ μ.map f ∈ M

Tactic:
simp only [null_iff_toMeasure_null, mem_ofPred_eq, toMeasure_map, M]

Goal state after:
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 : FiniteMeasure Ω
⊢ (Measure.map f ↑μ) (range f)ᶜ = 0
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
s : Set Ω'
hs : MeasurableSet s
⊢ (Measure.map f ↑(comap f ↑μ)) s = ↑↑μ s

Tactic:
Measure.map_apply hf.continuous.measurable hs

Goal state after:
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
s : Set Ω'
hs : MeasurableSet s
⊢ ↑(comap f ↑μ) (f ⁻¹' s) = ↑↑μ s
Module: Mathlib.Probability.Independence.Basic

Goal state before:
pp: Ω : Type u_1
ι : Type u_2
π : ι → Set (Set Ω)
x✝ : MeasurableSpace Ω
μ : Measure Ω
s : ι → Set Ω
inst✝ : Fintype ι
h : iIndepSets π μ
hs : ∀ (i : ι), s i ∈ π i
⊢ μ (⋂ i, s i) = ∏ i, μ (s i)

Tactic:
simp [← h.meas_biInter _ fun _i _ ↦ hs _]

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

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
β : ι → Type u_8
mβ : (i : ι) → MeasurableSpace (β i)
f g : (i : ι) → Ω → β i
h : ∀ (i : ι), ∀ᵐ (a : α) ∂μ, f i =ᵐ[κ a] g i
h' : iIndepFun g κ μ
i : ι
⊢ ∀ᵐ (a : α) ∂μ, g i =ᵐ[κ a] f i

Tactic:
filter_upwards [h i] with a ha using ha.symm

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

Goal state before:
pp: case inr.compl
α : Type u_1
Ω : Type u_2
_mα : MeasurableSpace α
m₂ m : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
inst✝ : IsZeroOrMarkovKernel κ
p1 p2 : Set (Set Ω)
h2 : m₂ ≤ m
hp2 : IsPiSystem p2
hpm2 : m₂ = generateFrom p2
hyp : IndepSets p1 p2 κ μ
t1 t2 : Set Ω
ht1 : t1 ∈ p1
ht1m : MeasurableSet t1
h : IsMarkovKernel κ
u : Set Ω
hu : MeasurableSet u
ihu : ∀ᵐ (a : α) ∂μ, (κ a) (t1 ∩ u) = (κ a) t1 * (κ a) u
⊢ ∀ᵐ (a : α) ∂μ, (κ a) (t1 ∩ uᶜ) = (κ a) t1 * (κ a) uᶜ

Tactic:
| compl u hu ihu =>

Goal state after:
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: Ω : Type u_1
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
μ : Measure Ω
μs : ℕ → Measure Ω
inst✝ : ∀ (i : ℕ), IsProbabilityMeasure (μs i)
f : Ω →ᵇ ℝ
f_nn : 0 ≤ f
h_opens : ∀ (G : Set Ω), IsOpen[inst✝²] G → μ G ≤ liminf (fun i ↦ (μs i) G) atTop
same : ∫⁻ (x : Ω), ENNReal.ofReal (f x) ∂μ ≤ liminf (fun i ↦ ∫⁻ (x : Ω), ENNReal.ofReal (f x) ∂μs i) atTop
obs : ∀ (i : ℕ), ∫⁻ (x : Ω), ↑‖f x‖₊ ∂μs i ≤ ↑‖f‖₊ * (μs i) univ
⊢ liminf (fun i ↦ ∫⁻ (x : Ω), ENNReal.ofReal (f x) ∂μs i) atTop < ∞

Tactic:
simp only [measure_univ, mul_one] at obs

Goal state after:
Ω : Type u_1
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
μ : Measure Ω
μs : ℕ → Measure Ω
inst✝ : ∀ (i : ℕ), IsProbabilityMeasure (μs i)
f : Ω →ᵇ ℝ
f_nn : 0 ≤ f
h_opens : ∀ (G : Set Ω), IsOpen[inst✝²] G → μ G ≤ liminf (fun i ↦ (μs i) G) atTop
same : ∫⁻ (x : Ω), ENNReal.ofReal (f x) ∂μ ≤ liminf (fun i ↦ ∫⁻ (x : Ω), ENNReal.ofReal (f x) ∂μs i) atTop
obs : ∀ (i : ℕ), ∫⁻ (x : Ω), ↑‖f x‖₊ ∂μs i ≤ ↑‖f‖₊
⊢ liminf (fun i ↦ ∫⁻ (x : Ω), ENNReal.ofReal (f x) ∂μs i) atTop < ∞
Module: Mathlib.Probability.Independence.Kernel.IndepFun

Goal state before:
pp: case refine_2
α : Type u_1
Ω : Type u_2
β : Type u_4
β' : Type u_5
γ : Type u_6
γ' : Type u_7
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
f : Ω → β
g : Ω → β'
mβ : MeasurableSpace β
mβ' : MeasurableSpace β'
mγ : MeasurableSpace γ
mγ' : MeasurableSpace γ'
φ : β → γ
ψ : β' → γ'
hfg : IndepFun f g κ μ
hf : AEMeasurable f (⇑κ ∘ₘ μ)
hg : AEMeasurable g (⇑κ ∘ₘ μ)
hφ : AEMeasurable φ (Measure.map f (⇑κ ∘ₘ μ))
hψ : AEMeasurable ψ (Measure.map g (⇑κ ∘ₘ μ))
h : IndepFun (AEMeasurable.mk φ hφ ∘ f) (AEMeasurable.mk ψ hψ ∘ g) κ μ
hφ_ae : ∀ᵐ (x : Ω) ∂⇑κ ∘ₘ μ, φ (f x) = AEMeasurable.mk φ hφ (f x)
hψ_ae : ∀ᵐ (x : Ω) ∂⇑κ ∘ₘ μ, ψ (g x) = AEMeasurable.mk ψ hψ (g x)
⊢ ∀ᵐ (a : α) ∂μ, AEMeasurable.mk ψ hψ ∘ g =ᵐ[κ a] ψ ∘ g

Tactic:
filter_upwards [Measure.ae_ae_of_ae_comp (hψ_ae)] with a haψ

Goal state after:
α : Type u_1
Ω : Type u_2
β : Type u_4
β' : Type u_5
γ : Type u_6
γ' : Type u_7
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
f : Ω → β
g : Ω → β'
mβ : MeasurableSpace β
mβ' : MeasurableSpace β'
mγ : MeasurableSpace γ
mγ' : MeasurableSpace γ'
φ : β → γ
ψ : β' → γ'
hfg : IndepFun f g κ μ
hf : AEMeasurable f (⇑κ ∘ₘ μ)
hg : AEMeasurable g (⇑κ ∘ₘ μ)
hφ : AEMeasurable φ (Measure.map f (⇑κ ∘ₘ μ))
hψ : AEMeasurable ψ (Measure.map g (⇑κ ∘ₘ μ))
h : IndepFun (AEMeasurable.mk φ hφ ∘ f) (AEMeasurable.mk ψ hψ ∘ g) κ μ
hφ_ae : ∀ᵐ (x : Ω) ∂⇑κ ∘ₘ μ, φ (f x) = AEMeasurable.mk φ hφ (f x)
hψ_ae : ∀ᵐ (x : Ω) ∂⇑κ ∘ₘ μ, ψ (g x) = AEMeasurable.mk ψ hψ (g x)
a : α
haψ : ∀ᵐ (ω : Ω) ∂κ a, ψ (g ω) = AEMeasurable.mk ψ hψ (g ω)
⊢ AEMeasurable.mk ψ hψ ∘ g =ᵐ[κ a] ψ ∘ 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
s : ι → Set (Set Ω)
S T : Set ι
h_indep : iIndepSets s κ μ
hST : Disjoint S T
t1 t2 : Set Ω
p1 : Finset ι
hp1 : ↑p1 ⊆ S
f1 : ι → Set Ω
ht1_m : ∀ x ∈ p1, f1 x ∈ s x
ht1_eq : t1 = ⋂ x ∈ p1, f1 x
p2 : Finset ι
hp2 : ↑p2 ⊆ T
f2 : ι → Set Ω
ht2_m : ∀ x ∈ p2, f2 x ∈ s x
ht2_eq : t2 = ⋂ x ∈ p2, f2 x
g : ι → Set Ω := fun i ↦ (if i ∈ p1 then f1 i else univ) ∩ if i ∈ p2 then f2 i else univ
h_P_inter✝ : ∀ᵐ (a : α) ∂μ, (κ a) (t1 ∩ t2) = ∏ n ∈ p1 ∪ p2, (κ a) (g n)
a : α
h_P_inter : (κ a) (t1 ∩ t2) = ∏ n ∈ p1 ∪ p2, (κ a) (g n)
ha1 : (κ a) (⋂ i ∈ p1, f1 i) = ∏ i ∈ p1, (κ a) (f1 i)
ha2 : (κ a) (⋂ i ∈ p2, f2 i) = ∏ i ∈ p2, (κ a) (f2 i)
h' : IsProbabilityMeasure (κ a)
h_μg : ∀ (n : ι), (κ a) (g n) = (if n ∈ p1 then (κ a) (f1 n) else 1) * if n ∈ p2 then (κ a) (f2 n) else 1
⊢ (κ a) (t1 ∩ t2) = (κ a) t1 * (κ a) t2

Tactic:
h_P_inter,

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
s : ι → Set (Set Ω)
S T : Set ι
h_indep : iIndepSets s κ μ
hST : Disjoint S T
t1 t2 : Set Ω
p1 : Finset ι
hp1 : ↑p1 ⊆ S
f1 : ι → Set Ω
ht1_m : ∀ x ∈ p1, f1 x ∈ s x
ht1_eq : t1 = ⋂ x ∈ p1, f1 x
p2 : Finset ι
hp2 : ↑p2 ⊆ T
f2 : ι → Set Ω
ht2_m : ∀ x ∈ p2, f2 x ∈ s x
ht2_eq : t2 = ⋂ x ∈ p2, f2 x
g : ι → Set Ω := fun i ↦ (if i ∈ p1 then f1 i else univ) ∩ if i ∈ p2 then f2 i else univ
h_P_inter✝ : ∀ᵐ (a : α) ∂μ, (κ a) (t1 ∩ t2) = ∏ n ∈ p1 ∪ p2, (κ a) (g n)
a : α
h_P_inter : (κ a) (t1 ∩ t2) = ∏ n ∈ p1 ∪ p2, (κ a) (g n)
ha1 : (κ a) (⋂ i ∈ p1, f1 i) = ∏ i ∈ p1, (κ a) (f1 i)
ha2 : (κ a) (⋂ i ∈ p2, f2 i) = ∏ i ∈ p2, (κ a) (f2 i)
h' : IsProbabilityMeasure (κ a)
h_μg : ∀ (n : ι), (κ a) (g n) = (if n ∈ p1 then (κ a) (f1 n) else 1) * if n ∈ p2 then (κ a) (f2 n) else 1
⊢ ∏ n ∈ p1 ∪ p2, (κ a) (g n) = (κ a) t1 * (κ a) t2
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)
a : ι
S : Finset ι
hp_ind : iIndepSets π κ μ
haS : a ∉ S
t1 t2 : Set Ω
s : Finset ι
hs_mem : s ⊆ S
ft1 : ι → Set Ω
hft1_mem : ∀ x ∈ s, ft1 x ∈ π x
ht1_eq : t1 = ⋂ x ∈ s, ft1 x
ht2_mem_pia : t2 ∈ π a
f : ι → Set Ω := fun n ↦ if n = a then t2 else if n ∈ s then ft1 n else univ
⊢ ∀ᵐ (a : α) ∂μ, (κ a) (t1 ∩ t2) = (κ a) t1 * (κ a) t2

Tactic:
have h_f_mem : ∀ n ∈ insert a s, f n ∈ π n := by
    intro n hn_mem_insert
    dsimp only [f]
    rcases Finset.mem_insert.mp hn_mem_insert with hn_mem | hn_mem
    · simp [hn_mem, ht2_mem_pia]
    · grind

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)
a : ι
S : Finset ι
hp_ind : iIndepSets π κ μ
haS : a ∉ S
t1 t2 : Set Ω
s : Finset ι
hs_mem : s ⊆ S
ft1 : ι → Set Ω
hft1_mem : ∀ x ∈ s, ft1 x ∈ π x
ht1_eq : t1 = ⋂ x ∈ s, ft1 x
ht2_mem_pia : t2 ∈ π a
f : ι → Set Ω := fun n ↦ if n = a then t2 else if n ∈ s then ft1 n else univ
h_f_mem : ∀ n ∈ insert a s, f n ∈ π n
⊢ ∀ᵐ (a : α) ∂μ, (κ a) (t1 ∩ t2) = (κ a) t1 * (κ a) t2
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
x : ι
hx : x ∈ S
⊢ x ∈ insert a S

Tactic:
simp [hx]

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

Goal state before:
pp: case add
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 g : Ω → ℝ≥0∞⦄,
    Disjoint (Function.support f) (Function.support g) →
      Measurable f →
        Measurable g →
          ∫⁻ (ω : Ω), f ω * T.indicator (fun x ↦ c) ω ∂μ =
              (∫⁻ (ω : Ω), f ω ∂μ) * ∫⁻ (ω : Ω), T.indicator (fun x ↦ c) ω ∂μ →
            ∫⁻ (ω : Ω), g ω * T.indicator (fun x ↦ c) ω ∂μ =
                (∫⁻ (ω : Ω), g ω ∂μ) * ∫⁻ (ω : Ω), T.indicator (fun x ↦ c) ω ∂μ →
              ∫⁻ (ω : Ω), (f + g) ω * T.indicator (fun x ↦ c) ω ∂μ =
                (∫⁻ (ω : Ω), (f + g) ω ∂μ) * ∫⁻ (ω : Ω), T.indicator (fun x ↦ c) ω ∂μ

Tactic:
intro f' g _ h_meas_f' _ h_ind_f' h_ind_g
    have h_measM_f' : Measurable f' := h_meas_f'.mono hMf le_rfl
    simp_rw [Pi.add_apply, right_distrib]
    rw [lintegral_add_left (h_mul_indicator _ h_measM_f'), lintegral_add_left h_measM_f',
      right_distrib, h_ind_f', h_ind_g]

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

Goal state before:
pp: case refine_2.hC
α : Type u_1
Ω : Type u_2
_mα : MeasurableSpace α
m₁ m₂ x✝ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
h_indep : Indep m₁ m₂ κ μ
s✝ t : Set Ω
hs✝ : MeasurableSet s✝
ht : MeasurableSet t
s' t' : Set Ω
hs' : s' ∈ {s | MeasurableSet s}
s : Set Ω
hs : s ∈ {t}
ht✝ : MeasurableSet s
⊢ s ∈ {s | MeasurableSet s}

Tactic:
| hC s hs =>

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

Goal state before:
pp: Ω : Type u_1
f : Ω → ℝ≥0∞
Mf Mg mΩ : MeasurableSpace Ω
μ : Measure Ω
hMf : Mf ≤ mΩ
hMg : Mg ≤ mΩ
h_ind : Indep Mf Mg μ
h_meas_f : Measurable f
h_measM_f : Measurable f
c : ℝ≥0∞
s : Set Ω
h_s : MeasurableSet s
⊢ {s} ⊆ {s | MeasurableSet s}

Tactic:
singleton_subset_iff

Goal state after:
Ω : Type u_1
f : Ω → ℝ≥0∞
Mf Mg mΩ : MeasurableSpace Ω
μ : Measure Ω
hMf : Mf ≤ mΩ
hMg : Mg ≤ mΩ
h_ind : Indep Mf Mg μ
h_meas_f : Measurable f
h_measM_f : Measurable f
c : ℝ≥0∞
s : Set Ω
h_s : MeasurableSet s
⊢ s ∈ {s | MeasurableSet s}
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
⊢ cov[X, Y; μ] = ∫ (x : Ω), (X * Y) x ∂μ - (∫ (x : Ω), X x ∂μ) * ∫ (x : Ω), Y x ∂μ

Tactic:
simp_rw [covariance, sub_mul, mul_sub]
   repeat rw [integral_sub]
   · simp_rw [integral_mul_const, integral_const_mul, integral_const, probReal_univ,
       one_smul]
     simp
   · exact hY.const_mul _ |>.integrable (by simp)
   · exact integrable_const _
   · exact hX.integrable_mul hY
   · exact hX.mul_const _ |>.integrable (by simp)
   · exact (hX.integrable_mul hY).sub (hX.mul_const _ |>.integrable (by simp))
   · exact (hY.const_mul _ |>.integrable (by simp)).sub (integrable_const _)

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

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
E : Type u_5
F : Type u_6
G : Type u_7
inst✝⁹ : TopologicalSpace E
inst✝⁸ : ContinuousENorm E
inst✝⁷ : MeasurableSpace E
inst✝⁶ : OpensMeasurableSpace E
inst✝⁵ : TopologicalSpace F
inst✝⁴ : ContinuousENorm F
inst✝³ : MeasurableSpace F
inst✝² : OpensMeasurableSpace F
inst✝¹ : TopologicalSpace G
inst✝ : ContinuousENorm G
X : Ω → E
Y : Ω → F
hXY : X ⟂ᵢ[μ] Y
hX : Integrable X μ
hY : Integrable Y μ
B : E → F → G
cB : Continuous (Function.uncurry B)
C : ℝ≥0
hB : ∀ (x : E) (y : F), ‖B x y‖ₑ ≤ ↑C * ‖x‖ₑ * ‖y‖ₑ
⊢ ↑C * ∫⁻ (ω : Ω), ‖X ω‖ₑ * ‖Y ω‖ₑ ∂μ = ↑C * ((∫⁻ (ω : Ω), ‖X ω‖ₑ ∂μ) * ∫⁻ (ω : Ω), ‖Y ω‖ₑ ∂μ)

Tactic:
rw [lintegral_mul_eq_lintegral_mul_lintegral_of_indepFun'' hX.1.enorm hY.1.enorm
        (hXY.comp measurable_enorm measurable_enorm)]

Goal state after:
Module: Mathlib.Probability.Moments.Variance

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

Tactic:
by_cases hℒ : MemLp X 2 μ

Goal state after:
case pos
Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
X : Ω → ℝ
hX : AEStronglyMeasurable X μ
hℒ : MemLp X 2 μ
⊢ eVar[X; μ] = ∫⁻ (ω : Ω), ‖X ω‖ₑ ^ 2 ∂μ - ENNReal.ofReal ((∫ (x : Ω), X x ∂μ) ^ 2)

case neg
Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
X : Ω → ℝ
hX : AEStronglyMeasurable X μ
hℒ : ¬MemLp X 2 μ
⊢ eVar[X; μ] = ∫⁻ (ω : Ω), ‖X ω‖ₑ ^ 2 ∂μ - ENNReal.ofReal ((∫ (x : Ω), X x ∂μ) ^ 2)
Module: Mathlib.Probability.Moments.Variance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
ι : Type u_3
X : ι → Ω → ℝ
s : Finset ι
hs : ∀ i ∈ s, MemLp (X i) 2 μ
h : (↑s).Pairwise fun i j ↦ X i ⟂ᵢ[μ] X j
⊢ Var[∑ i ∈ s, X i; μ] = ∑ i ∈ s, Var[X i; μ]

Tactic:
by_cases h'' : ∀ i ∈ s, X i =ᵐ[μ] 0
  · rw [variance_congr (Y := 0), variance_zero]
    · symm
      refine Finset.sum_eq_zero fun i hi ↦ ?_
      simp [variance_congr (h'' i hi)]
    · have := fun (i : s) ↦ h'' i.1 i.2
      filter_upwards [ae_all_iff.2 this] with ω hω
      simp only [Finset.sum_apply, Pi.zero_apply]
      exact Finset.sum_eq_zero fun i hi ↦ hω ⟨i, hi⟩
  obtain ⟨j, hj1, hj2⟩ := not_forall₂.1 h''
  obtain rfl | h' := s.eq_singleton_or_nontrivial hj1
  · simp
  obtain ⟨k, hk1, hk2⟩ := h'.exists_ne j
  have := (hs j hj1).isProbabilityMeasure_of_indepFun (X j) (X k) (by simp) (by simp) hj2
    (h hj1 hk1 hk2.symm)
  rw [variance_sum' hs]
  refine Finset.sum_congr rfl (fun i hi ↦ ?_)
  rw [← covariance_self (hs i hi).aemeasurable]
  refine Finset.sum_eq_single_of_mem i hi fun j hj1 hj2 ↦ ?_
  exact (h hi hj1 hj2.symm).covariance_eq_zero (hs i hi) (hs j hj1)

Goal state after:
Module: Mathlib.MeasureTheory.Function.ConvergenceInDistribution

Goal state before:
pp: ι : Type u_1
E : Type u_2
Ω' : Type u_3
Ω : ι → Type u_5
m : (i : ι) → MeasurableSpace (Ω i)
μ : (i : ι) → Measure (Ω i)
inst✝³ : ∀ (i : ι), IsProbabilityMeasure (μ i)
m' : MeasurableSpace Ω'
μ' : Measure Ω'
inst✝² : IsProbabilityMeasure μ'
mE : MeasurableSpace E
X : (i : ι) → Ω i → E
Z : Ω' → E
l : Filter ι
inst✝¹ : TopologicalSpace E
inst✝ : IsEmpty E
⊢ Tendsto (fun n ↦ ⟨Measure.map (X n) (μ n), ⋯⟩) l (𝓝 ⟨Measure.map Z μ', ⋯⟩)

Tactic:
simp only [Subsingleton.elim _ (0 : Measure E)]
    exact tendsto_const_nhds

Goal state after:
Module: Mathlib.MeasureTheory.Function.ConvergenceInDistribution

Goal state before:
pp: ι : Type u_1
E : Type u_2
Ω' : Type u_3
Ω'' : Type u_4
Ω : ι → Type u_5
m : (i : ι) → MeasurableSpace (Ω i)
μ : (i : ι) → Measure (Ω i)
inst✝⁶ : ∀ (i : ι), IsProbabilityMeasure (μ i)
m' : MeasurableSpace Ω'
μ' : Measure Ω'
inst✝⁵ : IsProbabilityMeasure μ'
m'' : MeasurableSpace Ω''
μ'' : Measure Ω''
inst✝⁴ : IsProbabilityMeasure μ''
mE : MeasurableSpace E
l : Filter ι
inst✝³ : TopologicalSpace E
inst✝² : HasOuterApproxClosed E
inst✝¹ : BorelSpace E
X : (i : ι) → Ω i → E
Z : Ω' → E
W : Ω'' → E
inst✝ : l.NeBot
h1 : TendstoInDistribution X l Z μ μ'
h2 : TendstoInDistribution X l W μ μ''
⊢ Measure.map Z μ' = Measure.map W μ''

Tactic:
have h_eq := tendsto_nhds_unique h1.tendsto h2.tendsto

Goal state after:
ι : Type u_1
E : Type u_2
Ω' : Type u_3
Ω'' : Type u_4
Ω : ι → Type u_5
m : (i : ι) → MeasurableSpace (Ω i)
μ : (i : ι) → Measure (Ω i)
inst✝⁶ : ∀ (i : ι), IsProbabilityMeasure (μ i)
m' : MeasurableSpace Ω'
μ' : Measure Ω'
inst✝⁵ : IsProbabilityMeasure μ'
m'' : MeasurableSpace Ω''
μ'' : Measure Ω''
inst✝⁴ : IsProbabilityMeasure μ''
mE : MeasurableSpace E
l : Filter ι
inst✝³ : TopologicalSpace E
inst✝² : HasOuterApproxClosed E
inst✝¹ : BorelSpace E
X : (i : ι) → Ω i → E
Z : Ω' → E
W : Ω'' → E
inst✝ : l.NeBot
h1 : TendstoInDistribution X l Z μ μ'
h2 : TendstoInDistribution X l W μ μ''
h_eq : ⟨Measure.map Z μ', ⋯⟩ = ⟨Measure.map W μ'', ⋯⟩
⊢ Measure.map Z μ' = Measure.map W μ''
Module: Mathlib.Probability.IdentDistrib

Goal state before:
pp: α : Type u_1
inst✝⁴ : MeasurableSpace α
E : Type u_5
inst✝³ : MeasurableSpace E
inst✝² : NormedAddCommGroup E
inst✝¹ : BorelSpace E
μ : Measure α
inst✝ : IsFiniteMeasure μ
ι : Type u_6
f : ι → α → E
j : ι
p : ℝ≥0∞
hp : 1 ≤ p
hp' : p ≠ ∞
hℒp : MemLp (f j) p μ
hfmeas : ∀ (i : ι), StronglyMeasurable (f i)
hf : ∀ (i : ι), IdentDistrib (f i) (f j) μ μ
⊢ UniformIntegrable f p μ

Tactic:
refine uniformIntegrable_of' hp hp' hfmeas fun ε hε => ?_

Goal state after:
α : Type u_1
inst✝⁴ : MeasurableSpace α
E : Type u_5
inst✝³ : MeasurableSpace E
inst✝² : NormedAddCommGroup E
inst✝¹ : BorelSpace E
μ : Measure α
inst✝ : IsFiniteMeasure μ
ι : Type u_6
f : ι → α → E
j : ι
p : ℝ≥0∞
hp : 1 ≤ p
hp' : p ≠ ∞
hℒp : MemLp (f j) p μ
hfmeas : ∀ (i : ι), StronglyMeasurable (f i)
hf : ∀ (i : ι), IdentDistrib (f i) (f j) μ μ
ε : ℝ
hε : 0 < ε
⊢ ∃ C, ∀ (i : ι), eLpNorm ({x | C ≤ ‖f i x‖₊}.indicator (f i)) p μ ≤ ENNReal.ofReal ε
Module: Mathlib.Probability.Independence.Integration

Goal state before:
pp: Ω : Type u_5
𝓧 : Type u_6
m mΩ : MeasurableSpace Ω
P : Measure Ω
m𝓧 : MeasurableSpace 𝓧
X : Ω → 𝓧
A : Set Ω
E : Type u_7
inst✝¹ : NormedAddCommGroup E
inst✝ : NormedSpace ℝ E
hm : m ≤ mΩ
f : 𝓧 → E
hA1 : Indep m (MeasurableSpace.comap X m𝓧) P
hX : AEMeasurable X P
hA2 : MeasurableSet A
hf : AEStronglyMeasurable f (Measure.map X P)
⊢ ∫ (ω : Ω) in A, f (X ω) ∂P = ∫ (ω : Ω), id (A.indicator 1 ω) • f (X ω) ∂P

Tactic:
rw [← integral_indicator (hm A hA2)]

Goal state after:
Ω : Type u_5
𝓧 : Type u_6
m mΩ : MeasurableSpace Ω
P : Measure Ω
m𝓧 : MeasurableSpace 𝓧
X : Ω → 𝓧
A : Set Ω
E : Type u_7
inst✝¹ : NormedAddCommGroup E
inst✝ : NormedSpace ℝ E
hm : m ≤ mΩ
f : 𝓧 → E
hA1 : Indep m (MeasurableSpace.comap X m𝓧) P
hX : AEMeasurable X P
hA2 : MeasurableSet A
hf : AEStronglyMeasurable f (Measure.map X P)
⊢ ∫ (x : Ω), A.indicator (fun ω ↦ f (X ω)) x ∂P = ∫ (ω : Ω), id (A.indicator 1 ω) • f (X ω) ∂P
Module: Mathlib.Probability.Process.Filtration

Goal state before:
pp: Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
inst✝ : Preorder ι
s : Set (Filtration ι m)
i j : ι
hij : i ≤ j
⊢ (fun i ↦ if s.Nonempty then sInf ((fun f ↦ ↑f i) '' s) else m) i ≤
    (fun i ↦ if s.Nonempty then sInf ((fun f ↦ ↑f i) '' s) else m) j

Tactic:
by_cases h_nonempty : Set.Nonempty s

Goal state after:
case pos
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
inst✝ : Preorder ι
s : Set (Filtration ι m)
i j : ι
hij : i ≤ j
h_nonempty : s.Nonempty
⊢ (fun i ↦ if s.Nonempty then sInf ((fun f ↦ ↑f i) '' s) else m) i ≤
    (fun i ↦ if s.Nonempty then sInf ((fun f ↦ ↑f i) '' s) else m) j

case neg
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
inst✝ : Preorder ι
s : Set (Filtration ι m)
i j : ι
hij : i ≤ j
h_nonempty : ¬s.Nonempty
⊢ (fun i ↦ if s.Nonempty then sInf ((fun f ↦ ↑f i) '' s) else m) i ≤
    (fun i ↦ if s.Nonempty then sInf ((fun f ↦ ↑f i) '' s) else m) j
Module: Mathlib.Probability.Process.Filtration

Goal state before:
pp: Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
inst✝ : Preorder ι
s : Set (Filtration ι m)
i : ι
⊢ (if s.Nonempty then sInf ((fun f ↦ ↑f i) '' s) else m) ≤ m

Tactic:
by_cases h_nonempty : Set.Nonempty s

Goal state after:
case pos
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
inst✝ : Preorder ι
s : Set (Filtration ι m)
i : ι
h_nonempty : s.Nonempty
⊢ (if s.Nonempty then sInf ((fun f ↦ ↑f i) '' s) else m) ≤ m

case neg
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
inst✝ : Preorder ι
s : Set (Filtration ι m)
i : ι
h_nonempty : ¬s.Nonempty
⊢ (if s.Nonempty then sInf ((fun f ↦ ↑f i) '' s) else m) ≤ m
Module: Mathlib.Probability.Process.Filtration

Goal state before:
pp: case pos
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
inst✝ : PartialOrder ι
𝓕 : Filtration ι m
this : TopologicalSpace ι := Preorder.topology ι
i : ι
hi : (𝓝[>] i).NeBot
⊢ ⨅ j, ⨅ (_ : j > i), ↑𝓕 j ≤ m

Tactic:
obtain ⟨j, hj⟩ := (frequently_gt_nhds i).exists
        exact iInf₂_le_of_le j hj (𝓕.le j)

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

Goal state before:
pp: case neg
Ω : Type u_1
ι : Type u_2
m : MeasurableSpace Ω
inst✝ : PartialOrder ι
𝓕 : Filtration ι m
this✝ : TopologicalSpace ι := Preorder.topology ι
this : OrderTopology ι
i : ι
hne : (𝓝[>] i).NeBot
u : ι
hu : u > i
hiou : Set.Ioo i u ∈ 𝓝[>] i
v : ι
hv : v ∈ Set.Ioo i u
hle₁ : ⨅ j, ⨅ (_ : j > i), ↑𝓕₊ j ≤ ↑𝓕₊ v
hnv : ¬(𝓝[>] v).NeBot
⊢ ↑𝓕₊ v ≤ ↑𝓕 u

Tactic:
· simpa [rightCont_apply, hnv] using 𝓕.mono hv.2.le

Goal state after: