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

Goal state before:
pp: case mp
Ω : Type u_1
inst✝⁴ : MeasurableSpace Ω
inst✝³ : TopologicalSpace Ω
inst✝² : OpensMeasurableSpace 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)
⊢ (∀ (F : Set Ω), IsClosed[inst✝³] F → limsup (fun i ↦ (μs i) F) L ≤ μ F) →
    ∀ (G : Set Ω), IsOpen[inst✝³] G → μ G ≤ liminf (fun i ↦ (μs i) G) L

Tactic:
intro h

Goal state after:
case mp
Ω : Type u_1
inst✝⁴ : MeasurableSpace Ω
inst✝³ : TopologicalSpace Ω
inst✝² : OpensMeasurableSpace 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)
h : ∀ (F : Set Ω), IsClosed[inst✝³] F → limsup (fun i ↦ (μs i) F) L ≤ μ F
⊢ ∀ (G : Set Ω), IsOpen[inst✝³] G → μ G ≤ liminf (fun i ↦ (μs i) G) L
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: case mpr
Ω : Type u_1
inst✝⁴ : MeasurableSpace Ω
inst✝³ : TopologicalSpace Ω
inst✝² : OpensMeasurableSpace 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)
⊢ (∀ (G : Set Ω), IsOpen[inst✝³] G → μ G ≤ liminf (fun i ↦ (μs i) G) L) →
    ∀ (F : Set Ω), IsClosed[inst✝³] F → limsup (fun i ↦ (μs i) F) L ≤ μ F

Tactic:
intro h

Goal state after:
case mpr
Ω : Type u_1
inst✝⁴ : MeasurableSpace Ω
inst✝³ : TopologicalSpace Ω
inst✝² : OpensMeasurableSpace 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)
h : ∀ (G : Set Ω), IsOpen[inst✝³] G → μ G ≤ liminf (fun i ↦ (μs i) G) L
⊢ ∀ (F : Set Ω), IsClosed[inst✝³] F → limsup (fun i ↦ (μs i) F) L ≤ μ F
Module: Mathlib.Probability.Independence.Kernel.IndepFun

Goal state before:
pp: case mp
α : Type u_1
Ω : Type u_2
β : Type u_4
β' : Type u_5
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'
⊢ IndepFun f g κ μ →
    ∀ (s : Set β) (t : Set β'),
      MeasurableSet s → MeasurableSet t → ∀ᵐ (a : α) ∂μ, (κ a) (f ⁻¹' s ∩ g ⁻¹' t) = (κ a) (f ⁻¹' s) * (κ a) (g ⁻¹' t)

Tactic:
intro h

Goal state after:
case mp
α : Type u_1
Ω : Type u_2
β : Type u_4
β' : Type u_5
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'
h : IndepFun f g κ μ
⊢ ∀ (s : Set β) (t : Set β'),
    MeasurableSet s → MeasurableSet t → ∀ᵐ (a : α) ∂μ, (κ a) (f ⁻¹' s ∩ g ⁻¹' t) = (κ a) (f ⁻¹' s) * (κ a) (g ⁻¹' t)
Module: Mathlib.Probability.Independence.Kernel.IndepFun

Goal state before:
pp: case mpr
α : Type u_1
Ω : Type u_2
β : Type u_4
β' : Type u_5
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'
⊢ (∀ (s : Set β) (t : Set β'),
      MeasurableSet s →
        MeasurableSet t → ∀ᵐ (a : α) ∂μ, (κ a) (f ⁻¹' s ∩ g ⁻¹' t) = (κ a) (f ⁻¹' s) * (κ a) (g ⁻¹' t)) →
    IndepFun f g κ μ

Tactic:
intro h

Goal state after:
case mpr
α : Type u_1
Ω : Type u_2
β : Type u_4
β' : Type u_5
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'
h :
  ∀ (s : Set β) (t : Set β'),
    MeasurableSet s → MeasurableSet t → ∀ᵐ (a : α) ∂μ, (κ a) (f ⁻¹' s ∩ g ⁻¹' t) = (κ a) (f ⁻¹' s) * (κ a) (g ⁻¹' t)
⊢ IndepFun f g κ μ
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
⊢ (∀ (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)) →
    iIndepFun f κ μ

Tactic:
intro h

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)
⊢ iIndepFun f κ μ
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: case calc_2
Ω : Type u_1
inst✝² : MeasurableSpace Ω
inst✝¹ : TopologicalSpace Ω
inst✝ : OpensMeasurableSpace Ω
μ : Measure Ω
μs : ℕ → Measure Ω
f : Ω → ℝ
f_cont : Continuous[inst✝¹, _] f
f_nn : 0 ≤ f
h_opens : ∀ (G : Set Ω), IsOpen[inst✝¹] G → μ G ≤ liminf (fun i ↦ (μs i) G) atTop
⊢ ∫⁻ (t : ℝ) in Ioi 0, liminf (fun i ↦ (μs i) {a | t < f a}) atTop ≤
    liminf (fun i ↦ ∫⁻ (t : ℝ) in Ioi 0, (μs i) {a | t < f a}) atTop

Tactic:
· -- (ii)
    exact lintegral_liminf_le (fun n ↦ Antitone.measurable (fun s t hst ↦
            measure_mono (fun ω hω ↦ lt_of_le_of_lt hst hω)))

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

Goal state before:
pp: case mp
Ω : Type u_1
β : Type u_6
β' : Type u_7
_mΩ : MeasurableSpace Ω
μ : Measure Ω
f : Ω → β
g : Ω → β'
mβ : MeasurableSpace β
mβ' : MeasurableSpace β'
hf : AEMeasurable f μ
hg : AEMeasurable g μ
σf : SigmaFinite (Measure.map f μ)
σg : SigmaFinite (Measure.map g μ)
h₀ :
  ∀ {s : Set β} {t : Set β'},
    MeasurableSet s →
      MeasurableSet t →
        μ (f ⁻¹' s) * μ (g ⁻¹' t) = (Measure.map f μ) s * (Measure.map g μ) t ∧
          μ (f ⁻¹' s ∩ g ⁻¹' t) = (Measure.map (fun ω ↦ (f ω, g ω)) μ) (s ×ˢ t)
h : ∀ (s : Set β) (t : Set β'), MeasurableSet s → MeasurableSet t → μ (f ⁻¹' s ∩ g ⁻¹' t) = μ (f ⁻¹' s) * μ (g ⁻¹' t)
s : Set β
t : Set β'
hs : MeasurableSet s
ht : MeasurableSet t
⊢ μ (f ⁻¹' s ∩ g ⁻¹' t) = μ (f ⁻¹' s) * μ (g ⁻¹' t)

Tactic:
h s t hs ht

Goal state after:
case mp
Ω : Type u_1
β : Type u_6
β' : Type u_7
_mΩ : MeasurableSpace Ω
μ : Measure Ω
f : Ω → β
g : Ω → β'
mβ : MeasurableSpace β
mβ' : MeasurableSpace β'
hf : AEMeasurable f μ
hg : AEMeasurable g μ
σf : SigmaFinite (Measure.map f μ)
σg : SigmaFinite (Measure.map g μ)
h₀ :
  ∀ {s : Set β} {t : Set β'},
    MeasurableSet s →
      MeasurableSet t →
        μ (f ⁻¹' s) * μ (g ⁻¹' t) = (Measure.map f μ) s * (Measure.map g μ) t ∧
          μ (f ⁻¹' s ∩ g ⁻¹' t) = (Measure.map (fun ω ↦ (f ω, g ω)) μ) (s ×ˢ t)
h : ∀ (s : Set β) (t : Set β'), MeasurableSet s → MeasurableSet t → μ (f ⁻¹' s ∩ g ⁻¹' t) = μ (f ⁻¹' s) * μ (g ⁻¹' t)
s : Set β
t : Set β'
hs : MeasurableSet s
ht : MeasurableSet t
⊢ μ (f ⁻¹' s) * μ (g ⁻¹' t) = μ (f ⁻¹' s) * μ (g ⁻¹' t)
Module: Mathlib.Probability.Independence.Basic

Goal state before:
pp: case mpr
Ω : Type u_1
β : Type u_6
β' : Type u_7
_mΩ : MeasurableSpace Ω
μ : Measure Ω
f : Ω → β
g : Ω → β'
mβ : MeasurableSpace β
mβ' : MeasurableSpace β'
hf : AEMeasurable f μ
hg : AEMeasurable g μ
σf : SigmaFinite (Measure.map f μ)
σg : SigmaFinite (Measure.map g μ)
h₀ :
  ∀ {s : Set β} {t : Set β'},
    MeasurableSet s →
      MeasurableSet t →
        μ (f ⁻¹' s) * μ (g ⁻¹' t) = (Measure.map f μ) s * (Measure.map g μ) t ∧
          μ (f ⁻¹' s ∩ g ⁻¹' t) = (Measure.map (fun ω ↦ (f ω, g ω)) μ) (s ×ˢ t)
⊢ Measure.map (fun ω ↦ (f ω, g ω)) μ = (Measure.map f μ).prod (Measure.map g μ) →
    ∀ (s : Set β) (t : Set β'), MeasurableSet s → MeasurableSet t → μ (f ⁻¹' s ∩ g ⁻¹' t) = μ (f ⁻¹' s) * μ (g ⁻¹' t)

Tactic:
intro h

Goal state after:
case mpr
Ω : Type u_1
β : Type u_6
β' : Type u_7
_mΩ : MeasurableSpace Ω
μ : Measure Ω
f : Ω → β
g : Ω → β'
mβ : MeasurableSpace β
mβ' : MeasurableSpace β'
hf : AEMeasurable f μ
hg : AEMeasurable g μ
σf : SigmaFinite (Measure.map f μ)
σg : SigmaFinite (Measure.map g μ)
h₀ :
  ∀ {s : Set β} {t : Set β'},
    MeasurableSet s →
      MeasurableSet t →
        μ (f ⁻¹' s) * μ (g ⁻¹' t) = (Measure.map f μ) s * (Measure.map g μ) t ∧
          μ (f ⁻¹' s ∩ g ⁻¹' t) = (Measure.map (fun ω ↦ (f ω, g ω)) μ) (s ×ˢ t)
h : Measure.map (fun ω ↦ (f ω, g ω)) μ = (Measure.map f μ).prod (Measure.map g μ)
⊢ ∀ (s : Set β) (t : Set β'), MeasurableSet s → MeasurableSet t → μ (f ⁻¹' s ∩ g ⁻¹' t) = μ (f ⁻¹' s) * μ (g ⁻¹' t)
Module: Mathlib.Probability.Independence.Kernel.IndepFun

Goal state before:
pp: case mp
α : Type u_1
Ω : Type u_2
β : Type u_4
β' : Type u_5
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'
inst✝ : IsZeroOrMarkovKernel κ
hf : Measurable f
hg : Measurable g
⊢ (∀ (s : Set β) (t : Set β'),
      MeasurableSet s →
        MeasurableSet t → ∀ᵐ (a : α) ∂μ, (κ a) (f ⁻¹' s ∩ g ⁻¹' t) = (κ a) (f ⁻¹' s) * (κ a) (g ⁻¹' t)) →
    ∀ (s : Set β) (t : Set β'), MeasurableSet s → MeasurableSet t → IndepSet (f ⁻¹' s) (g ⁻¹' t) κ μ

Tactic:
intro h

Goal state after:
case mp
α : Type u_1
Ω : Type u_2
β : Type u_4
β' : Type u_5
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'
inst✝ : IsZeroOrMarkovKernel κ
hf : Measurable f
hg : Measurable g
h :
  ∀ (s : Set β) (t : Set β'),
    MeasurableSet s → MeasurableSet t → ∀ᵐ (a : α) ∂μ, (κ a) (f ⁻¹' s ∩ g ⁻¹' t) = (κ a) (f ⁻¹' s) * (κ a) (g ⁻¹' t)
⊢ ∀ (s : Set β) (t : Set β'), MeasurableSet s → MeasurableSet t → IndepSet (f ⁻¹' s) (g ⁻¹' t) κ μ
Module: Mathlib.Probability.Independence.Kernel.IndepFun

Goal state before:
pp: case mpr
α : Type u_1
Ω : Type u_2
β : Type u_4
β' : Type u_5
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'
inst✝ : IsZeroOrMarkovKernel κ
hf : Measurable f
hg : Measurable g
⊢ (∀ (s : Set β) (t : Set β'), MeasurableSet s → MeasurableSet t → IndepSet (f ⁻¹' s) (g ⁻¹' t) κ μ) →
    ∀ (s : Set β) (t : Set β'),
      MeasurableSet s → MeasurableSet t → ∀ᵐ (a : α) ∂μ, (κ a) (f ⁻¹' s ∩ g ⁻¹' t) = (κ a) (f ⁻¹' s) * (κ a) (g ⁻¹' t)

Tactic:
intro h

Goal state after:
case mpr
α : Type u_1
Ω : Type u_2
β : Type u_4
β' : Type u_5
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'
inst✝ : IsZeroOrMarkovKernel κ
hf : Measurable f
hg : Measurable g
h : ∀ (s : Set β) (t : Set β'), MeasurableSet s → MeasurableSet t → IndepSet (f ⁻¹' s) (g ⁻¹' t) κ μ
⊢ ∀ (s : Set β) (t : Set β'),
    MeasurableSet s → MeasurableSet t → ∀ᵐ (a : α) ∂μ, (κ a) (f ⁻¹' s ∩ g ⁻¹' t) = (κ a) (f ⁻¹' s) * (κ a) (g ⁻¹' t)
Module: Mathlib.Probability.Independence.Basic

Goal state before:
pp: Ω : Type u_1
ι : Type u_2
_mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝¹ : Fintype ι
β : ι → Type u_11
m : (i : ι) → MeasurableSpace (β i)
f : (i : ι) → Ω → β i
inst✝ : IsProbabilityMeasure μ
hf : ∀ (i : ι), AEMeasurable (f i) μ
h₀ :
  ∀ {s : (i : ι) → Set (β i)},
    (∀ (i : ι), MeasurableSet (s i)) →
      ∏ i, μ (f i ⁻¹' s i) = ∏ i, (Measure.map (f i) μ) (s i) ∧
        μ (⋂ i, f i ⁻¹' s i) = (Measure.map (fun ω i ↦ f i ω) μ) (univ.pi s)
⊢ (Measure.map (fun ω i ↦ f i ω) μ = Measure.pi fun i ↦ Measure.map (f i) μ) →
    ∀ (S : Finset ι) {sets : (i : ι) → Set (β i)},
      (∀ i ∈ S, MeasurableSet (sets i)) → μ (⋂ i ∈ S, f i ⁻¹' sets i) = ∏ i ∈ S, μ (f i ⁻¹' sets i)

Tactic:
intro h

Goal state after:
Ω : Type u_1
ι : Type u_2
_mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝¹ : Fintype ι
β : ι → Type u_11
m : (i : ι) → MeasurableSpace (β i)
f : (i : ι) → Ω → β i
inst✝ : IsProbabilityMeasure μ
hf : ∀ (i : ι), AEMeasurable (f i) μ
h₀ :
  ∀ {s : (i : ι) → Set (β i)},
    (∀ (i : ι), MeasurableSet (s i)) →
      ∏ i, μ (f i ⁻¹' s i) = ∏ i, (Measure.map (f i) μ) (s i) ∧
        μ (⋂ i, f i ⁻¹' s i) = (Measure.map (fun ω i ↦ f i ω) μ) (univ.pi s)
h : Measure.map (fun ω i ↦ f i ω) μ = Measure.pi fun i ↦ Measure.map (f i) μ
⊢ ∀ (S : Finset ι) {sets : (i : ι) → Set (β i)},
    (∀ i ∈ S, MeasurableSet (sets i)) → μ (⋂ i ∈ S, f i ⁻¹' sets i) = ∏ i ∈ S, μ (f i ⁻¹' sets 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
⊢ 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:
intro h

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.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)
⊢ (κ a) (⋂ i ∈ p2, f2 i) = ∏ i ∈ p2, (κ a) (f2 i) → IsProbabilityMeasure (κ a) → (κ a) (t1 ∩ t2) = (κ a) t1 * (κ a) t2

Tactic:
ha2

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)
⊢ IsProbabilityMeasure (κ a) → (κ a) (t1 ∩ t2) = (κ a) t1 * (κ a) t2
Module: Mathlib.Probability.Density

Goal state before:
pp: Ω : Type u_1
E : Type u_2
inst✝ : MeasurableSpace E
m : MeasurableSpace Ω
ℙ : Measure Ω
μ : Measure E
X : Ω → E
hac : map X ℙ ≪ μ
hpdf : ¬pdf X ℙ μ =ᵐ[μ] 0
⊢ HasPDF X ℙ μ

Tactic:
refine ⟨?_, ?_, hac⟩

Goal state after:
case refine_1
Ω : Type u_1
E : Type u_2
inst✝ : MeasurableSpace E
m : MeasurableSpace Ω
ℙ : Measure Ω
μ : Measure E
X : Ω → E
hac : map X ℙ ≪ μ
hpdf : ¬pdf X ℙ μ =ᵐ[μ] 0
⊢ AEMeasurable X ℙ

case refine_2
Ω : Type u_1
E : Type u_2
inst✝ : MeasurableSpace E
m : MeasurableSpace Ω
ℙ : Measure Ω
μ : Measure E
X : Ω → E
hac : map X ℙ ≪ μ
hpdf : ¬pdf X ℙ μ =ᵐ[μ] 0
⊢ (map X ℙ).HaveLebesgueDecomposition 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)
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
h_t1 : t1 = ⋂ n ∈ s, f n
h_μ_t1 : ∀ᵐ (a' : α) ∂μ, (κ a') t1 = ∏ n ∈ s, (κ a') (f n)
h_t2 : t2 = f a
h_μ_inter : ∀ᵐ (a' : α) ∂μ, (κ a') (t1 ∩ t2) = ∏ n ∈ insert a s, (κ a') (f n)
has : a ∉ s
a' : α
ha1 : (κ a') t1 = ∏ n ∈ s, (κ a') (f n)
⊢ (κ a') (t1 ∩ t2) = ∏ n ∈ insert a s, (κ a') (f n) → (κ a') (t1 ∩ t2) = (κ a') t1 * (κ a') t2

Tactic:
ha2

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
h_f_mem_pi : ∀ n ∈ s, f n ∈ π n
h_t1 : t1 = ⋂ n ∈ s, f n
h_μ_t1 : ∀ᵐ (a' : α) ∂μ, (κ a') t1 = ∏ n ∈ s, (κ a') (f n)
h_t2 : t2 = f a
h_μ_inter : ∀ᵐ (a' : α) ∂μ, (κ a') (t1 ∩ t2) = ∏ n ∈ insert a s, (κ a') (f n)
has : a ∉ s
a' : α
ha1 : (κ a') t1 = ∏ n ∈ s, (κ a') (f n)
ha2 : (κ a') (t1 ∩ t2) = ∏ n ∈ insert a s, (κ a') (f n)
⊢ (κ a') (t1 ∩ t2) = (κ a') t1 * (κ a') t2
Module: Mathlib.MeasureTheory.Measure.Portmanteau

Goal state before:
pp: case refine_2
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))
n : ℕ
hn : r < ν (accumulate f n)
⊢ ⋃ t ∈ Finset.image f (Finset.range (n + 1)), t ⊆ G

Tactic:
simpa [G_eq] using fun i _ ↦ subset_iUnion f i

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

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
hX : MemLp X 2 μ
this : (∫⁻ (x : Ω), ‖X x - ∫ (x : Ω), X x ∂μ‖ₑ ^ 2 ∂μ) ^ 1 < ∞
⊢ eVar[X; μ] < ∞

Tactic:
ENNReal.rpow_one

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
hX : MemLp X 2 μ
this : ∫⁻ (x : Ω), ‖X x - ∫ (x : Ω), X x ∂μ‖ₑ ^ 2 ∂μ < ∞
⊢ eVar[X; 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
⊢ Var[fun ω ↦ X ω + c; μ] = Var[X; μ]

Tactic:
have hX_int : Integrable X μ := hX_Lp.integrable one_le_two

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
⊢ Var[fun ω ↦ X ω + c; μ] = Var[X; μ]
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
c : ℝ
⊢ cov[fun ω ↦ X ω / c, Y; μ] = cov[X, Y; μ] / c

Tactic:
← inv_mul_eq_div,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
c : ℝ
⊢ cov[fun ω ↦ c⁻¹ * X ω, Y; μ] = c⁻¹ * cov[X, Y; μ]
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
c : ℝ
⊢ cov[X, fun ω ↦ Y ω / c; μ] = cov[X, Y; μ] / c

Tactic:
← inv_mul_eq_div,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
c : ℝ
⊢ cov[X, fun ω ↦ c⁻¹ * Y ω; μ] = c⁻¹ * cov[X, Y; μ]
Module: Mathlib.Probability.Moments.Covariance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
X Y : Ω → ℝ
μ : Measure Ω
c : ℝ
⊢ cov[X, fun ω ↦ c⁻¹ * Y ω; μ] = c⁻¹ * cov[X, Y; μ]

Tactic:
covariance_const_mul_right

Goal state after:
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
⊢ ¬Integrable (fun a ↦ (X a - ∫ (x : Ω), X x ∂μ) ^ 2) μ

Tactic:
intro h

Goal state after:
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
h : Integrable (fun a ↦ (X a - ∫ (x : Ω), X x ∂μ) ^ 2) μ
⊢ False
Module: Mathlib.Probability.Moments.Variance

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
a b : ℝ
X : Ω → ℝ
h : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
hX : AEMeasurable X μ
ha : ∀ᵐ (ω : Ω) ∂μ, a ≤ X ω
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ≤ b
hX_int₂ : Integrable (fun ω ↦ -X ω ^ 2) μ
hX_int₁ : Integrable (fun ω ↦ (a + b) * X ω) μ
h0 : 0 ≤ -∫ (x : Ω), (X ^ 2) x ∂μ + (a + b) * ∫ (x : Ω), X x ∂μ - a * b
⊢ Var[X; μ] ≤ (a + b) * ∫ (x : Ω), X x ∂μ - a * b - (∫ (x : Ω), X x ∂μ) ^ 2

Tactic:
rw [variance_eq_sub (memLp_of_bounded h hX.aestronglyMeasurable 2)]

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsProbabilityMeasure μ
a b : ℝ
X : Ω → ℝ
h : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
hX : AEMeasurable X μ
ha : ∀ᵐ (ω : Ω) ∂μ, a ≤ X ω
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ≤ b
hX_int₂ : Integrable (fun ω ↦ -X ω ^ 2) μ
hX_int₁ : Integrable (fun ω ↦ (a + b) * X ω) μ
h0 : 0 ≤ -∫ (x : Ω), (X ^ 2) x ∂μ + (a + b) * ∫ (x : Ω), X x ∂μ - a * b
⊢ ∫ (x : Ω), (X ^ 2) x ∂μ - (∫ (x : Ω), X x ∂μ) ^ 2 ≤ (a + b) * ∫ (x : Ω), X x ∂μ - a * b - (∫ (x : Ω), X x ∂μ) ^ 2
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
m : (i : ι) → MeasurableSpace (β i)
f : (i : ι) → Ω → β i
hf_indep : iIndepFun f κ μ
hf_meas : ∀ (i : ι), AEMeasurable (f i) (⇑κ ∘ₘ μ)
i j k l : ι
hik : i ≠ k
hil : i ≠ l
hjk : j ≠ k
hjl : j ≠ l
h :
  IndepFun (fun a ↦ (AEMeasurable.mk (f i) ⋯ a, AEMeasurable.mk (f j) ⋯ a))
    (fun a ↦ (AEMeasurable.mk (f k) ⋯ a, AEMeasurable.mk (f l) ⋯ a)) κ μ
a : α
hk : ∀ᵐ (ω : Ω) ∂κ a, f k ω = AEMeasurable.mk (f k) ⋯ ω
hl : ∀ᵐ (ω : Ω) ∂κ a, f l ω = AEMeasurable.mk (f l) ⋯ ω
ω : Ω
hωk : f k ω = AEMeasurable.mk (f k) ⋯ ω
⊢ f l ω = AEMeasurable.mk (f l) ⋯ ω → (AEMeasurable.mk (f k) ⋯ ω, AEMeasurable.mk (f l) ⋯ ω) = (f k ω, f l ω)

Tactic:
hωl

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
β : ι → Type u_8
m : (i : ι) → MeasurableSpace (β i)
f : (i : ι) → Ω → β i
hf_indep : iIndepFun f κ μ
hf_meas : ∀ (i : ι), AEMeasurable (f i) (⇑κ ∘ₘ μ)
i j k l : ι
hik : i ≠ k
hil : i ≠ l
hjk : j ≠ k
hjl : j ≠ l
h :
  IndepFun (fun a ↦ (AEMeasurable.mk (f i) ⋯ a, AEMeasurable.mk (f j) ⋯ a))
    (fun a ↦ (AEMeasurable.mk (f k) ⋯ a, AEMeasurable.mk (f l) ⋯ a)) κ μ
a : α
hk : ∀ᵐ (ω : Ω) ∂κ a, f k ω = AEMeasurable.mk (f k) ⋯ ω
hl : ∀ᵐ (ω : Ω) ∂κ a, f l ω = AEMeasurable.mk (f l) ⋯ ω
ω : Ω
hωk : f k ω = AEMeasurable.mk (f k) ⋯ ω
hωl : f l ω = AEMeasurable.mk (f l) ⋯ ω
⊢ (AEMeasurable.mk (f k) ⋯ ω, AEMeasurable.mk (f l) ⋯ ω) = (f k ω, f l ω)
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 : Type u_8
m : MeasurableSpace β
inst✝¹ : Mul β
inst✝ : MeasurableMul₂ β
f : ι → Ω → β
hf_indep : iIndepFun f κ μ
hf_meas : ∀ (i : ι), AEMeasurable (f i) (⇑κ ∘ₘ μ)
i j k : ι
hik : i ≠ k
hjk : j ≠ k
⊢ IndepFun (f i * f j) (f k) κ μ

Tactic:
have : IndepFun (fun ω => (f i ω, f j ω)) (f k) κ μ :=
    hf_indep.indepFun_prodMk₀ hf_meas i j k hik hjk

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
β : Type u_8
m : MeasurableSpace β
inst✝¹ : Mul β
inst✝ : MeasurableMul₂ β
f : ι → Ω → β
hf_indep : iIndepFun f κ μ
hf_meas : ∀ (i : ι), AEMeasurable (f i) (⇑κ ∘ₘ μ)
i j k : ι
hik : i ≠ k
hjk : j ≠ k
this : IndepFun (fun ω ↦ (f i ω, f j ω)) (f k) κ μ
⊢ IndepFun (f i * f j) (f k) κ μ