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

Goal state before:
pp: Ω : Type u_1
ι : Type u_2
L : Filter ι
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
inst✝ : HasOuterApproxClosed Ω
μ : ProbabilityMeasure Ω
μs : ι → ProbabilityMeasure Ω
μs_lim : Tendsto μs L (𝓝 μ)
E : Set Ω
E_nullbdry : μ (frontier E) = 0
⊢ Tendsto (fun i ↦ (μs i) E) L (𝓝 (μ E))

Tactic:
have key := tendsto_measure_of_null_frontier_of_tendsto' μs_lim (by simpa using E_nullbdry)

Goal state after:
Ω : Type u_1
ι : Type u_2
L : Filter ι
inst✝³ : MeasurableSpace Ω
inst✝² : TopologicalSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
inst✝ : HasOuterApproxClosed Ω
μ : ProbabilityMeasure Ω
μs : ι → ProbabilityMeasure Ω
μs_lim : Tendsto μs L (𝓝 μ)
E : Set Ω
E_nullbdry : μ (frontier E) = 0
key : Tendsto (fun i ↦ ↑(μs i) E) L (𝓝 (↑μ E))
⊢ Tendsto (fun i ↦ (μs i) E) L (𝓝 (μ E))
Module: Mathlib.MeasureTheory.Function.UniformIntegrable

Goal state before:
pp: case refine_3.inr
α : Type u_1
m : MeasurableSpace α
μ : Measure α
p : ℝ≥0∞
E : Type u_4
inst✝¹ : NormedAddCommGroup E
inst✝ : NormedSpace ℝ E
hp : 1 ≤ p
f : ℕ → α → E
hf₁ : ∀ (i : ℕ), AEStronglyMeasurable (f i) μ
hf₂ : UnifIntegrable f p μ
C : ℝ≥0
hC : ∀ (i : ℕ), eLpNorm (f i) p μ ≤ ↑C
n : ℕ
hn : n ≠ 0
⊢ ‖(↑n)⁻¹‖ₑ * ∑ i ∈ Finset.range n, eLpNorm (f i) p μ ≤ ↑C

Tactic:
enorm_inv (by positivity),

Goal state after:
case refine_3.inr
α : Type u_1
m : MeasurableSpace α
μ : Measure α
p : ℝ≥0∞
E : Type u_4
inst✝¹ : NormedAddCommGroup E
inst✝ : NormedSpace ℝ E
hp : 1 ≤ p
f : ℕ → α → E
hf₁ : ∀ (i : ℕ), AEStronglyMeasurable (f i) μ
hf₂ : UnifIntegrable f p μ
C : ℝ≥0
hC : ∀ (i : ℕ), eLpNorm (f i) p μ ≤ ↑C
n : ℕ
hn : n ≠ 0
⊢ ‖↑n‖ₑ⁻¹ * ∑ i ∈ Finset.range n, eLpNorm (f i) p μ ≤ ↑C
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: case inr
Ω : Type u_1
ι : Type u_2
L : Filter ι
inst✝⁴ : MeasurableSpace Ω
inst✝³ : TopologicalSpace Ω
inst✝² : PseudoMetrizableSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
μs : ι → Measure Ω
h : ∀ {E : Set Ω}, MeasurableSet E → μ (frontier E) = 0 → Tendsto (fun i ↦ (μs i) E) L (𝓝 (μ E))
F : Set Ω
F_closed : IsClosed[inst✝³] F
this : PseudoMetricSpace Ω := pseudoMetrizableSpacePseudoMetric Ω
h✝ : L.NeBot
ex : ∃ rs, Tendsto rs atTop (𝓝 0) ∧ ∀ (n : ℕ), 0 < rs n ∧ μ (frontier (Metric.thickening (rs n) F)) = 0
rs : ℕ → ℝ := Classical.choose ex
rs_lim : Tendsto rs atTop (𝓝 0)
rs_pos : ∀ (n : ℕ), 0 < rs n
rs_null : ∀ (n : ℕ), μ (frontier (Metric.thickening (rs n) F)) = 0
Fthicks_open : ∀ (n : ℕ), IsOpen[inst✝³] (Metric.thickening (rs n) F)
key : ∀ (n : ℕ), Tendsto (fun i ↦ (μs i) (Metric.thickening (rs n) F)) L (𝓝 (μ (Metric.thickening (rs n) F)))
ε : ℝ≥0
⊢ 0 < ε → μ F < ∞ → limsup (fun i ↦ (μs i) F) L ≤ μ F + ↑ε

Tactic:
ε_pos

Goal state after:
case inr
Ω : Type u_1
ι : Type u_2
L : Filter ι
inst✝⁴ : MeasurableSpace Ω
inst✝³ : TopologicalSpace Ω
inst✝² : PseudoMetrizableSpace Ω
inst✝¹ : OpensMeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
μs : ι → Measure Ω
h : ∀ {E : Set Ω}, MeasurableSet E → μ (frontier E) = 0 → Tendsto (fun i ↦ (μs i) E) L (𝓝 (μ E))
F : Set Ω
F_closed : IsClosed[inst✝³] F
this : PseudoMetricSpace Ω := pseudoMetrizableSpacePseudoMetric Ω
h✝ : L.NeBot
ex : ∃ rs, Tendsto rs atTop (𝓝 0) ∧ ∀ (n : ℕ), 0 < rs n ∧ μ (frontier (Metric.thickening (rs n) F)) = 0
rs : ℕ → ℝ := Classical.choose ex
rs_lim : Tendsto rs atTop (𝓝 0)
rs_pos : ∀ (n : ℕ), 0 < rs n
rs_null : ∀ (n : ℕ), μ (frontier (Metric.thickening (rs n) F)) = 0
Fthicks_open : ∀ (n : ℕ), IsOpen[inst✝³] (Metric.thickening (rs n) F)
key : ∀ (n : ℕ), Tendsto (fun i ↦ (μs i) (Metric.thickening (rs n) F)) L (𝓝 (μ (Metric.thickening (rs n) F)))
ε : ℝ≥0
ε_pos : 0 < ε
⊢ μ F < ∞ → limsup (fun i ↦ (μs i) F) L ≤ μ F + ↑ε
Module: Mathlib.Probability.Independence.Kernel.IndepFun

Goal state before:
pp: α : Type u_1
Ω : Type u_2
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
ι : Type u_8
β : ι → Type u_9
m : (x : ι) → MeasurableSpace (β x)
f : (i : ι) → Ω → β i
h :
  ∀ (S : Finset ι) {sets : (i : ι) → Set (β i)},
    (∀ i ∈ S, MeasurableSet (sets i)) → ∀ᵐ (a : α) ∂μ, (κ a) (⋂ i ∈ S, f i ⁻¹' sets i) = ∏ i ∈ S, (κ a) (f i ⁻¹' sets i)
S : Finset ι
setsΩ : ι → Set Ω
h_meas : ∀ i ∈ S, setsΩ i ∈ (fun x ↦ {s | MeasurableSet s}) i
setsβ : (i : ι) → Set (β i) := fun i ↦ if hi_mem : i ∈ S then Exists.choose ⋯ else univ
i : ι
hi_mem : i ∈ S
⊢ MeasurableSet (setsβ i)

Tactic:
setsβ,

Goal state after:
α : Type u_1
Ω : Type u_2
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
ι : Type u_8
β : ι → Type u_9
m : (x : ι) → MeasurableSpace (β x)
f : (i : ι) → Ω → β i
h :
  ∀ (S : Finset ι) {sets : (i : ι) → Set (β i)},
    (∀ i ∈ S, MeasurableSet (sets i)) → ∀ᵐ (a : α) ∂μ, (κ a) (⋂ i ∈ S, f i ⁻¹' sets i) = ∏ i ∈ S, (κ a) (f i ⁻¹' sets i)
S : Finset ι
setsΩ : ι → Set Ω
h_meas : ∀ i ∈ S, setsΩ i ∈ (fun x ↦ {s | MeasurableSet s}) i
setsβ : (i : ι) → Set (β i) := fun i ↦ if hi_mem : i ∈ S then Exists.choose ⋯ else univ
i : ι
hi_mem : i ∈ S
⊢ MeasurableSet (if hi_mem : i ∈ S then Exists.choose ⋯ else univ)
Module: Mathlib.Probability.Independence.Kernel.IndepFun

Goal state before:
pp: α : Type u_1
Ω : Type u_2
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
ι : Type u_8
β : ι → Type u_9
m : (x : ι) → MeasurableSpace (β x)
f : (i : ι) → Ω → β i
h :
  ∀ (S : Finset ι) {sets : (i : ι) → Set (β i)},
    (∀ i ∈ S, MeasurableSet (sets i)) → ∀ᵐ (a : α) ∂μ, (κ a) (⋂ i ∈ S, f i ⁻¹' sets i) = ∏ i ∈ S, (κ a) (f i ⁻¹' sets i)
S : Finset ι
setsΩ : ι → Set Ω
h_meas : ∀ i ∈ S, setsΩ i ∈ (fun x ↦ {s | MeasurableSet s}) i
setsβ : (i : ι) → Set (β i) := fun i ↦ if hi_mem : i ∈ S then Exists.choose ⋯ else univ
h_measβ : ∀ i ∈ S, MeasurableSet (setsβ i)
i : ι
hi_mem : i ∈ S
⊢ setsΩ i = f i ⁻¹' setsβ i

Tactic:
setsβ,

Goal state after:
α : Type u_1
Ω : Type u_2
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
ι : Type u_8
β : ι → Type u_9
m : (x : ι) → MeasurableSpace (β x)
f : (i : ι) → Ω → β i
h :
  ∀ (S : Finset ι) {sets : (i : ι) → Set (β i)},
    (∀ i ∈ S, MeasurableSet (sets i)) → ∀ᵐ (a : α) ∂μ, (κ a) (⋂ i ∈ S, f i ⁻¹' sets i) = ∏ i ∈ S, (κ a) (f i ⁻¹' sets i)
S : Finset ι
setsΩ : ι → Set Ω
h_meas : ∀ i ∈ S, setsΩ i ∈ (fun x ↦ {s | MeasurableSet s}) i
setsβ : (i : ι) → Set (β i) := fun i ↦ if hi_mem : i ∈ S then Exists.choose ⋯ else univ
h_measβ : ∀ i ∈ S, MeasurableSet (setsβ i)
i : ι
hi_mem : i ∈ S
⊢ setsΩ i = f i ⁻¹' if hi_mem : i ∈ S then Exists.choose ⋯ else univ
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
f : ι → Set Ω
t : ι → Set β
s : Finset ι
inst✝ : Finite ι
hY : ∀ (i : ι), Measurable (Y i)
hindep : iIndepFun (fun i ω ↦ (X i ω, Y i ω)) μ
hf : ∀ i ∈ s, MeasurableSet (f i)
hy : ∀ i ∉ s, μ (Y i ⁻¹' t i) ≠ 0
ht : ∀ (i : ι), MeasurableSet (t i)
this : IsProbabilityMeasure μ
val✝ : Fintype ι
g : ι → Set Ω := fun i' ↦ if i' ∈ s then Y i' ⁻¹' t i' ∩ f i' else Y i' ⁻¹' t i'
⊢ ((⋂ i, Y i ⁻¹' t i) ∩ ⋂ i, if i ∈ s then f i else univ) = ⋂ i, Y i ⁻¹' t i ∩ if i ∈ s then f i else univ

Tactic:
Set.iInter_inter_distrib

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
f : ι → Set Ω
t : ι → Set β
s : Finset ι
inst✝ : Finite ι
hY : ∀ (i : ι), Measurable (Y i)
hindep : iIndepFun (fun i ω ↦ (X i ω, Y i ω)) μ
hf : ∀ i ∈ s, MeasurableSet (f i)
hy : ∀ i ∉ s, μ (Y i ⁻¹' t i) ≠ 0
ht : ∀ (i : ι), MeasurableSet (t i)
this : IsProbabilityMeasure μ
val✝ : Fintype ι
g : ι → Set Ω := fun i' ↦ if i' ∈ s then Y i' ⁻¹' t i' ∩ f i' else Y i' ⁻¹' t i'
⊢ ((⋂ i, Y i ⁻¹' t i) ∩ ⋂ i, if i ∈ s then f i else univ) = (⋂ i, Y i ⁻¹' t i) ∩ ⋂ i, if i ∈ s then f i else univ
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
hgm : ∀ i ∈ p1 ∪ p2, g i ∈ s i
h_p1_inter_p2 :
  (⋂ x ∈ p1, f1 x) ∩ ⋂ x ∈ p2, f2 x = ⋂ i ∈ p1 ∪ p2, (if i ∈ p1 then f1 i else univ) ∩ if i ∈ p2 then f2 i else univ
a : α
ha : (κ a) (⋂ i ∈ p1 ∪ p2, g i) = ∏ i ∈ p1 ∪ p2, (κ a) (g i)
⊢ (κ a) (t1 ∩ t2) = ∏ n ∈ p1 ∪ p2, (κ a) (g n)

Tactic:
ht1_eq,

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
hgm : ∀ i ∈ p1 ∪ p2, g i ∈ s i
h_p1_inter_p2 :
  (⋂ x ∈ p1, f1 x) ∩ ⋂ x ∈ p2, f2 x = ⋂ i ∈ p1 ∪ p2, (if i ∈ p1 then f1 i else univ) ∩ if i ∈ p2 then f2 i else univ
a : α
ha : (κ a) (⋂ i ∈ p1 ∪ p2, g i) = ∏ i ∈ p1 ∪ p2, (κ a) (g i)
⊢ (κ a) ((⋂ x ∈ p1, f1 x) ∩ t2) = ∏ n ∈ p1 ∪ p2, (κ a) (g n)
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
⊢ (∏ x ∈ p1, (κ a) (f1 x)) * ∏ x ∈ p2, (κ a) (f2 x) = (κ a) t1 * (κ a) t2

Tactic:
ht1_eq,

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
⊢ (∏ x ∈ p1, (κ a) (f1 x)) * ∏ x ∈ p2, (κ a) (f2 x) = (κ a) (⋂ x ∈ p1, f1 x) * (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
h_f_mem : ∀ n ∈ insert a s, f n ∈ π n
h_f_mem_pi : ∀ n ∈ s, f n ∈ π n
n : ι
hnS : n ∈ s
⊢ n ≠ a

Tactic:
rintro rfl

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)
S : Finset ι
hp_ind : iIndepSets π κ μ
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
n : ι
hnS : n ∈ s
haS : n ∉ S
ht2_mem_pia : t2 ∈ π n
f : ι → Set Ω := fun n_1 ↦ if n_1 = n then t2 else if n_1 ∈ s then ft1 n_1 else univ
h_f_mem : ∀ n_1 ∈ insert n s, f n_1 ∈ π n_1
h_f_mem_pi : ∀ n ∈ s, f n ∈ π n
⊢ False
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: case insert
Ω : Type u_1
mΩ : MeasurableSpace Ω
Y : Ω → ℝ
μ : Measure Ω
ι : Type u_2
X : ι → Ω → ℝ
s✝ : Finset ι
inst✝ : IsFiniteMeasure μ
hY : MemLp Y 2 μ
i : ι
s : Finset ι
hi : i ∉ s
h_ind : (∀ i ∈ s, MemLp (X i) 2 μ) → cov[∑ i ∈ s, X i, Y; μ] = ∑ i ∈ s, cov[X i, Y; μ]
hX : ∀ i_1 ∈ insert i s, MemLp (X i_1) 2 μ
⊢ ∀ i ∈ s, MemLp (X i) 2 μ

Tactic:
exact fun j hj ↦ hX j (by simp [hj])

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

Goal state before:
pp: case neg
Ω : Type u_1
𝕜 : Type u_2
inst✝¹⁴ : RCLike 𝕜
mΩ : MeasurableSpace Ω
μ : Measure Ω
𝓧 : Type u_3
𝓨 : Type u_4
E : Type u_5
F : Type u_6
G : Type u_7
inst✝¹³ : MeasurableSpace 𝓧
inst✝¹² : MeasurableSpace 𝓨
inst✝¹¹ : NormedAddCommGroup E
inst✝¹⁰ : NormedSpace ℝ E
inst✝⁹ : NormedSpace 𝕜 E
inst✝⁸ : CompleteSpace E
inst✝⁷ : NormedAddCommGroup F
inst✝⁶ : NormedSpace ℝ F
inst✝⁵ : NormedSpace 𝕜 F
inst✝⁴ : CompleteSpace F
inst✝³ : NormedAddCommGroup G
inst✝² : NormedSpace ℝ G
inst✝¹ : NormedSpace 𝕜 G
inst✝ : CompleteSpace G
X : Ω → 𝓧
Y : Ω → 𝓨
f : 𝓧 → E
g : 𝓨 → F
hXY : X ⟂ᵢ[μ] Y
hX : AEMeasurable X μ
hY : AEMeasurable Y μ
hf : Integrable f (Measure.map X μ)
hg : Integrable g (Measure.map Y μ)
B : E →L[𝕜] F →L[𝕜] G
h : ¬∀ᵐ (ω : Ω) ∂μ, f (X ω) = 0
this✝³ : MeasurableSpace E := borel E
this✝² : BorelSpace E
this✝¹ : MeasurableSpace F := borel F
this✝ : BorelSpace F
this : IsProbabilityMeasure μ
⊢ AEStronglyMeasurable (fun z ↦ (B (f z.1)) (g z.2)) ((Measure.map X μ).prod (Measure.map Y μ))

Tactic:
exact Continuous.comp_aestronglyMeasurable₂ (g := (B · ·)) (by fun_prop)
    hf.1.comp_fst hg.1.comp_snd

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

Goal state before:
pp: case e'_3
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 Y : (i : ι) → Ω i → E
Z : Ω' → E
l : Filter ι
inst✝¹ : TopologicalSpace E
inst✝ : OpensMeasurableSpace E
T : Ω' → E
hXY : ∀ (i : ι), X i =ᵐ[μ i] Y i
hZT : Z =ᵐ[μ'] T
h : TendstoInDistribution X l Z μ μ'
n : ι
⊢ ⟨Measure.map (Y n) (μ n), ⋯⟩ = ⟨Measure.map (X n) (μ n), ⋯⟩

pp: case e'_5
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 Y : (i : ι) → Ω i → E
Z : Ω' → E
l : Filter ι
inst✝¹ : TopologicalSpace E
inst✝ : OpensMeasurableSpace E
T : Ω' → E
hXY : ∀ (i : ι), X i =ᵐ[μ i] Y i
hZT : Z =ᵐ[μ'] T
h : TendstoInDistribution X l Z μ μ'
⊢ ⟨Measure.map T μ', ⋯⟩ = ⟨Measure.map Z μ', ⋯⟩

Tactic:
· simpa using Measure.map_congr (hXY n).symm

Goal state after:
case e'_5
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 Y : (i : ι) → Ω i → E
Z : Ω' → E
l : Filter ι
inst✝¹ : TopologicalSpace E
inst✝ : OpensMeasurableSpace E
T : Ω' → E
hXY : ∀ (i : ι), X i =ᵐ[μ i] Y i
hZT : Z =ᵐ[μ'] T
h : TendstoInDistribution X l Z μ μ'
⊢ ⟨Measure.map T μ', ⋯⟩ = ⟨Measure.map Z μ', ⋯⟩
Module: Mathlib.MeasureTheory.Function.ConvergenceInDistribution

Goal state before:
pp: ι : Type u_1
E : Type u_2
Ω' : Type u_3
m' : MeasurableSpace Ω'
μ' : Measure Ω'
inst✝³ : IsProbabilityMeasure μ'
mE : MeasurableSpace E
Z : Ω' → E
l : Filter ι
inst✝² : TopologicalSpace E
inst✝¹ : l.IsCountablyGenerated
inst✝ : OpensMeasurableSpace E
X : ι → Ω' → E
hX₁ : ∀ (i : ι), AEMeasurable (X i) μ'
hZ : AEMeasurable Z μ'
hX₂ : ∀ᵐ (ω : Ω') ∂μ', Tendsto (fun i ↦ X i ω) l (𝓝 (Z ω))
⊢ Tendsto (fun n ↦ ⟨Measure.map (X n) μ', ⋯⟩) l (𝓝 ⟨Measure.map Z μ', ⋯⟩)

Tactic:
ProbabilityMeasure.tendsto_iff_forall_lintegral_tendsto,

Goal state after:
ι : Type u_1
E : Type u_2
Ω' : Type u_3
m' : MeasurableSpace Ω'
μ' : Measure Ω'
inst✝³ : IsProbabilityMeasure μ'
mE : MeasurableSpace E
Z : Ω' → E
l : Filter ι
inst✝² : TopologicalSpace E
inst✝¹ : l.IsCountablyGenerated
inst✝ : OpensMeasurableSpace E
X : ι → Ω' → E
hX₁ : ∀ (i : ι), AEMeasurable (X i) μ'
hZ : AEMeasurable Z μ'
hX₂ : ∀ᵐ (ω : Ω') ∂μ', Tendsto (fun i ↦ X i ω) l (𝓝 (Z ω))
⊢ ∀ (f : BoundedContinuousFunction E NNReal),
    Tendsto (fun i ↦ ∫⁻ (ω : E), ↑(f ω) ∂↑⟨Measure.map (X i) μ', ⋯⟩) l (𝓝 (∫⁻ (ω : E), ↑(f ω) ∂↑⟨Measure.map Z μ', ⋯⟩))
Module: Mathlib.MeasureTheory.Function.ConvergenceInDistribution

Goal state before:
pp: case h_bound
ι : Type u_1
E : Type u_2
Ω' : Type u_3
m' : MeasurableSpace Ω'
μ' : Measure Ω'
inst✝³ : IsProbabilityMeasure μ'
mE : MeasurableSpace E
Z : Ω' → E
l : Filter ι
inst✝² : TopologicalSpace E
inst✝¹ : l.IsCountablyGenerated
inst✝ : OpensMeasurableSpace E
X : ι → Ω' → E
hX₁ : ∀ (i : ι), AEMeasurable (X i) μ'
hZ : AEMeasurable Z μ'
hX₂ : ∀ᵐ (ω : Ω') ∂μ', Tendsto (fun i ↦ X i ω) l (𝓝 (Z ω))
f : BoundedContinuousFunction E NNReal
⊢ ∀ᶠ (n : ι) in l, ∀ᵐ (a : Ω') ∂μ', ↑(f (X n a)) ≤ edist 0 f

pp: case h_fin
ι : Type u_1
E : Type u_2
Ω' : Type u_3
m' : MeasurableSpace Ω'
μ' : Measure Ω'
inst✝³ : IsProbabilityMeasure μ'
mE : MeasurableSpace E
Z : Ω' → E
l : Filter ι
inst✝² : TopologicalSpace E
inst✝¹ : l.IsCountablyGenerated
inst✝ : OpensMeasurableSpace E
X : ι → Ω' → E
hX₁ : ∀ (i : ι), AEMeasurable (X i) μ'
hZ : AEMeasurable Z μ'
hX₂ : ∀ᵐ (ω : Ω') ∂μ', Tendsto (fun i ↦ X i ω) l (𝓝 (Z ω))
f : BoundedContinuousFunction E NNReal
⊢ ∫⁻ (a : Ω'), edist 0 f ∂μ' ≠ ⊤

pp: case h_lim
ι : Type u_1
E : Type u_2
Ω' : Type u_3
m' : MeasurableSpace Ω'
μ' : Measure Ω'
inst✝³ : IsProbabilityMeasure μ'
mE : MeasurableSpace E
Z : Ω' → E
l : Filter ι
inst✝² : TopologicalSpace E
inst✝¹ : l.IsCountablyGenerated
inst✝ : OpensMeasurableSpace E
X : ι → Ω' → E
hX₁ : ∀ (i : ι), AEMeasurable (X i) μ'
hZ : AEMeasurable Z μ'
hX₂ : ∀ᵐ (ω : Ω') ∂μ', Tendsto (fun i ↦ X i ω) l (𝓝 (Z ω))
f : BoundedContinuousFunction E NNReal
⊢ ∀ᵐ (a : Ω') ∂μ', Tendsto (fun n ↦ ↑(f (X n a))) l (𝓝 ↑(f (Z a)))

Tactic:
· simp [f.apply_le_edist_zero]

Goal state after:
case h_fin
ι : Type u_1
E : Type u_2
Ω' : Type u_3
m' : MeasurableSpace Ω'
μ' : Measure Ω'
inst✝³ : IsProbabilityMeasure μ'
mE : MeasurableSpace E
Z : Ω' → E
l : Filter ι
inst✝² : TopologicalSpace E
inst✝¹ : l.IsCountablyGenerated
inst✝ : OpensMeasurableSpace E
X : ι → Ω' → E
hX₁ : ∀ (i : ι), AEMeasurable (X i) μ'
hZ : AEMeasurable Z μ'
hX₂ : ∀ᵐ (ω : Ω') ∂μ', Tendsto (fun i ↦ X i ω) l (𝓝 (Z ω))
f : BoundedContinuousFunction E NNReal
⊢ ∫⁻ (a : Ω'), edist 0 f ∂μ' ≠ ⊤

case h_lim
ι : Type u_1
E : Type u_2
Ω' : Type u_3
m' : MeasurableSpace Ω'
μ' : Measure Ω'
inst✝³ : IsProbabilityMeasure μ'
mE : MeasurableSpace E
Z : Ω' → E
l : Filter ι
inst✝² : TopologicalSpace E
inst✝¹ : l.IsCountablyGenerated
inst✝ : OpensMeasurableSpace E
X : ι → Ω' → E
hX₁ : ∀ (i : ι), AEMeasurable (X i) μ'
hZ : AEMeasurable Z μ'
hX₂ : ∀ᵐ (ω : Ω') ∂μ', Tendsto (fun i ↦ X i ω) l (𝓝 (Z ω))
f : BoundedContinuousFunction E NNReal
⊢ ∀ᵐ (a : Ω') ∂μ', Tendsto (fun n ↦ ↑(f (X n a))) l (𝓝 ↑(f (Z a)))
Module: Mathlib.Probability.Independence.Kernel.IndepFun

Goal state before:
pp: α : Type u_1
Ω : Type u_2
ι : Type u_3
β : Type u_4
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
mβ : MeasurableSpace β
X : ι → Ω → α
Y : ι → Ω → β
f : ι → Set Ω
t : ι → Set β
s : Finset ι
inst✝ : Finite ι
hY : ∀ (i : ι), Measurable (Y i)
hindep : iIndepFun (fun i ω ↦ (X i ω, Y i ω)) κ μ
hf : ∀ i ∈ s, MeasurableSet (f i)
hy✝ : ∀ᵐ (a : α) ∂μ, ∀ i ∉ s, (κ a) (Y i ⁻¹' t i) ≠ 0
ht : ∀ (i : ι), MeasurableSet (t i)
val✝ : Fintype ι
g : ι → Set Ω := fun i' ↦ if i' ∈ s then Y i' ⁻¹' t i' ∩ f i' else Y i' ⁻¹' t i'
hYt✝ : ∀ (i : ι), MeasurableSet (Y i ⁻¹' t i)
hg✝ : ∀ (i : ι), MeasurableSet (g i)
a : α
hy : ∀ i ∉ s, (κ a) (Y i ⁻¹' t i) ≠ 0
a✝ : IsProbabilityMeasure (κ a)
hYt : (κ a) (⋂ i, Y i ⁻¹' t i) = ∏ i, (κ a) (Y i ⁻¹' t i)
hg : (κ a) (⋂ i, g i) = ∏ i, (κ a) (g i)
⊢ ((⋂ i, Y i ⁻¹' t i) ∩ ⋂ i, if i ∈ s then f i else univ) = ⋂ i, Y i ⁻¹' t i ∩ if i ∈ s then f i else univ

Tactic:
Set.iInter_inter_distrib

Goal state after:
α : Type u_1
Ω : Type u_2
ι : Type u_3
β : Type u_4
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
κ : Kernel α Ω
μ : Measure α
mβ : MeasurableSpace β
X : ι → Ω → α
Y : ι → Ω → β
f : ι → Set Ω
t : ι → Set β
s : Finset ι
inst✝ : Finite ι
hY : ∀ (i : ι), Measurable (Y i)
hindep : iIndepFun (fun i ω ↦ (X i ω, Y i ω)) κ μ
hf : ∀ i ∈ s, MeasurableSet (f i)
hy✝ : ∀ᵐ (a : α) ∂μ, ∀ i ∉ s, (κ a) (Y i ⁻¹' t i) ≠ 0
ht : ∀ (i : ι), MeasurableSet (t i)
val✝ : Fintype ι
g : ι → Set Ω := fun i' ↦ if i' ∈ s then Y i' ⁻¹' t i' ∩ f i' else Y i' ⁻¹' t i'
hYt✝ : ∀ (i : ι), MeasurableSet (Y i ⁻¹' t i)
hg✝ : ∀ (i : ι), MeasurableSet (g i)
a : α
hy : ∀ i ∉ s, (κ a) (Y i ⁻¹' t i) ≠ 0
a✝ : IsProbabilityMeasure (κ a)
hYt : (κ a) (⋂ i, Y i ⁻¹' t i) = ∏ i, (κ a) (Y i ⁻¹' t i)
hg : (κ a) (⋂ i, g i) = ∏ i, (κ a) (g i)
⊢ ((⋂ i, Y i ⁻¹' t i) ∩ ⋂ i, if i ∈ s then f i else univ) = (⋂ i, Y i ⁻¹' t i) ∩ ⋂ i, if i ∈ s then f i else univ
Module: Mathlib.MeasureTheory.Function.Intersectivity

Goal state before:
pp: α : Type u_2
inst✝¹ : MeasurableSpace α
μ : Measure α
inst✝ : IsFiniteMeasure μ
r : ℝ≥0∞
s : ℕ → Set α
hs : ∀ (n : ℕ), MeasurableSet (s n)
hr₀ : r ≠ 0
hr : ∀ (n : ℕ), r ≤ μ (s n)
M : (α → ℝ) → Set α := fun f ↦ {x | eLpNormEssSup f μ < ↑‖f x‖₊}
N : Set α := ⋃ u, M ((⋂ n ∈ u, s n).indicator 1)
hN₀ : μ N = 0
hN₁ : ∀ (u : Finset ℕ), ((⋂ n ∈ u, s n) \ N).Nonempty → 0 < μ (⋂ n ∈ u, s n)
f : ℕ → α → ℝ≥0∞ := fun n ↦ (↑(n + 1))⁻¹ • ∑ k ∈ Finset.range (n + 1), (s k).indicator 1
hfapp : ∀ (n : ℕ) (a : α), f n a = (↑(n + 1))⁻¹ * ∑ k ∈ Finset.range (n + 1), (s k).indicator 1 a
hf : ∀ (n : ℕ), Measurable (f n)
hf₁ : ∀ (n : ℕ), f n ≤ 1
hrf : ∀ (n : ℕ), r ≤ ∫⁻ (a : α), f n a ∂μ
⊢ μ ≠ 0

Tactic:
rintro rfl

Goal state after:
α : Type u_2
inst✝¹ : MeasurableSpace α
r : ℝ≥0∞
s : ℕ → Set α
hs : ∀ (n : ℕ), MeasurableSet (s n)
hr₀ : r ≠ 0
f : ℕ → α → ℝ≥0∞ := fun n ↦ (↑(n + 1))⁻¹ • ∑ k ∈ Finset.range (n + 1), (s k).indicator 1
hfapp : ∀ (n : ℕ) (a : α), f n a = (↑(n + 1))⁻¹ * ∑ k ∈ Finset.range (n + 1), (s k).indicator 1 a
hf : ∀ (n : ℕ), Measurable (f n)
hf₁ : ∀ (n : ℕ), f n ≤ 1
inst✝ : IsFiniteMeasure 0
hr : ∀ (n : ℕ), r ≤ 0 (s n)
M : (α → ℝ) → Set α := fun f ↦ {x | eLpNormEssSup f 0 < ↑‖f x‖₊}
N : Set α := ⋃ u, M ((⋂ n ∈ u, s n).indicator 1)
hN₀ : 0 N = 0
hN₁ : ∀ (u : Finset ℕ), ((⋂ n ∈ u, s n) \ N).Nonempty → 0 < 0 (⋂ n ∈ u, s n)
hrf : ∀ (n : ℕ), r ≤ ∫⁻ (a : α), f n a ∂0
⊢ False