mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
hA : 0 ≤ A
n : ℕ
hn : n ≠ 0
M : MeasurableSet (Set.Ioc (-A) A)
⊢ ∫ (y : ℝ), (Set.Ioc (-A) A).indicator id y ^ n ∂Measure.map f μ =
    ∫ (x : ℝ), (Set.Ioc (-A) A).indicator (fun x ↦ x ^ n) x ∂Measure.map f μ

pp: α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
hA : 0 ≤ A
n : ℕ
hn : n ≠ 0
M : MeasurableSet (Set.Ioc (-A) A)
⊢ -A ≤ A

pp: α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
hA : 0 ≤ A
n : ℕ
hn : n ≠ 0
M : MeasurableSet (Set.Ioc (-A) A)
⊢ AEStronglyMeasurable (fun z ↦ (Set.Ioc (-A) A).indicator id z ^ n) (Measure.map f μ)

Tactic:
]

Goal state after:
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
hA : 0 ≤ A
n : ℕ
hn : n ≠ 0
M : MeasurableSet (Set.Ioc (-A) A)
⊢ ∫ (y : ℝ), (Set.Ioc (-A) A).indicator id y ^ n ∂Measure.map f μ =
    ∫ (x : ℝ), (Set.Ioc (-A) A).indicator (fun x ↦ x ^ n) x ∂Measure.map f μ

α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
hA : 0 ≤ A
n : ℕ
hn : n ≠ 0
M : MeasurableSet (Set.Ioc (-A) A)
⊢ -A ≤ A

α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
hA : 0 ≤ A
n : ℕ
hn : n ≠ 0
M : MeasurableSet (Set.Ioc (-A) A)
⊢ AEStronglyMeasurable (fun z ↦ (Set.Ioc (-A) A).indicator id z ^ n) (Measure.map f μ)
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
n : ℕ
hn : n ≠ 0
h'f : 0 ≤ f
M : MeasurableSet (Set.Ioc 0 A)
M' : MeasurableSet (Set.Ioc A 0)
⊢ ∫ (x : α), ((Set.Ioc 0 A).indicator id ∘ f) x ^ n ∂μ = ∫ (y : ℝ) in 0..A, y ^ n ∂Measure.map f μ

Tactic:
]

Goal state after:
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
n : ℕ
hn : n ≠ 0
h'f : 0 ≤ f
M : MeasurableSet (Set.Ioc 0 A)
M' : MeasurableSet (Set.Ioc A 0)
⊢ ∫ (x : α), ((Set.Ioc 0 A).indicator id ∘ f) x ^ n ∂μ = ∫ (y : ℝ) in 0..A, y ^ n ∂Measure.map f μ
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: case inl
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
n : ℕ
hn : n ≠ 0
h'f : 0 ≤ f
M : MeasurableSet (Set.Ioc 0 A)
M' : MeasurableSet (Set.Ioc A 0)
hA : 0 ≤ A
⊢ ∫ (y : ℝ), (Set.Ioc 0 A).indicator id y ^ n ∂Measure.map f μ =
    ∫ (x : ℝ), (Set.Ioc 0 A).indicator (fun x ↦ x ^ n) x ∂Measure.map f μ

pp: case inl
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
n : ℕ
hn : n ≠ 0
h'f : 0 ≤ f
M : MeasurableSet (Set.Ioc 0 A)
M' : MeasurableSet (Set.Ioc A 0)
hA : 0 ≤ A
⊢ AEStronglyMeasurable (fun z ↦ (Set.Ioc 0 A).indicator id z ^ n) (Measure.map f μ)

Tactic:
]

Goal state after:
case inl
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
n : ℕ
hn : n ≠ 0
h'f : 0 ≤ f
M : MeasurableSet (Set.Ioc 0 A)
M' : MeasurableSet (Set.Ioc A 0)
hA : 0 ≤ A
⊢ ∫ (y : ℝ), (Set.Ioc 0 A).indicator id y ^ n ∂Measure.map f μ =
    ∫ (x : ℝ), (Set.Ioc 0 A).indicator (fun x ↦ x ^ n) x ∂Measure.map f μ

case inl
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
n : ℕ
hn : n ≠ 0
h'f : 0 ≤ f
M : MeasurableSet (Set.Ioc 0 A)
M' : MeasurableSet (Set.Ioc A 0)
hA : 0 ≤ A
⊢ AEStronglyMeasurable (fun z ↦ (Set.Ioc 0 A).indicator id z ^ n) (Measure.map f μ)
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: case inr
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
n : ℕ
hn : n ≠ 0
h'f : 0 ≤ f
M : MeasurableSet (Set.Ioc 0 A)
M' : MeasurableSet (Set.Ioc A 0)
hA : A < 0
⊢ ∫ (y : ℝ), (Set.Ioc 0 A).indicator id y ^ n ∂Measure.map f μ =
    -∫ (x : ℝ), (Set.Ioc A 0).indicator (fun x ↦ x ^ n) x ∂Measure.map f μ

pp: case inr
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
n : ℕ
hn : n ≠ 0
h'f : 0 ≤ f
M : MeasurableSet (Set.Ioc 0 A)
M' : MeasurableSet (Set.Ioc A 0)
hA : A < 0
⊢ AEStronglyMeasurable (fun z ↦ (Set.Ioc 0 A).indicator id z ^ n) (Measure.map f μ)

Tactic:
]

Goal state after:
case inr
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
n : ℕ
hn : n ≠ 0
h'f : 0 ≤ f
M : MeasurableSet (Set.Ioc 0 A)
M' : MeasurableSet (Set.Ioc A 0)
hA : A < 0
⊢ ∫ (y : ℝ), (Set.Ioc 0 A).indicator id y ^ n ∂Measure.map f μ =
    -∫ (x : ℝ), (Set.Ioc A 0).indicator (fun x ↦ x ^ n) x ∂Measure.map f μ

case inr
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : AEStronglyMeasurable f μ
A : ℝ
n : ℕ
hn : n ≠ 0
h'f : 0 ≤ f
M : MeasurableSet (Set.Ioc 0 A)
M' : MeasurableSet (Set.Ioc A 0)
hA : A < 0
⊢ AEStronglyMeasurable (fun z ↦ (Set.Ioc 0 A).indicator id z ^ n) (Measure.map f μ)
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: case refine_2
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : Integrable f μ
A : ℝ
x : α
⊢ |truncation f A x| ≤ |f x|

Tactic:
]

Goal state after:
case refine_2
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : Integrable f μ
A : ℝ
x : α
⊢ |truncation f A x| ≤ |f x|
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
j : ℕ
hj : j ∈ range K
⊢ ∫ (x : ℝ) in ↑j..↑N, 1 ∂ρ = ∫ (x : ℝ) in ↑j..↑N, 1 ∂ρ

pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
j : ℕ
hj : j ∈ range K
⊢ ∀ k ∈ Set.Ico j N, IntervalIntegrable (fun x ↦ 1) ρ ↑k ↑(k + 1)

Tactic:
]

Goal state after:
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
j : ℕ
hj : j ∈ range K
⊢ ∀ k ∈ Set.Ico j N, IntervalIntegrable (fun x ↦ 1) ρ ↑k ↑(k + 1)
Module: Mathlib.Probability.Moments.SubGaussian

Goal state before:
pp: case pos.mgf_le
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
η : Kernel Ω Ω''
h : HasSubgaussianMGF Y cY η (⇑κ ∘ₘ ν)
hν : SFinite ν
hκ : IsSFiniteKernel κ
h2 : ∀ᵐ (ω' : Ω) ∂Measure.map Prod.snd (ν ⊗ₘ κ), ∀ (t : ℝ), mgf Y (η ω') t ≤ rexp (↑cY * t ^ 2 / 2)
⊢ ∀ᵐ (ω' : Ω' × Ω) ∂ν ⊗ₘ κ, ∀ (t : ℝ), mgf Y ((prodMkLeft Ω' η) ω') t ≤ rexp (↑cY * t ^ 2 / 2)

Tactic:
]

Goal state after:
case pos.mgf_le
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
η : Kernel Ω Ω''
h : HasSubgaussianMGF Y cY η (⇑κ ∘ₘ ν)
hν : SFinite ν
hκ : IsSFiniteKernel κ
h2 : ∀ᵐ (ω' : Ω) ∂Measure.map Prod.snd (ν ⊗ₘ κ), ∀ (t : ℝ), mgf Y (η ω') t ≤ rexp (↑cY * t ^ 2 / 2)
⊢ ∀ᵐ (ω' : Ω' × Ω) ∂ν ⊗ₘ κ, ∀ (t : ℝ), mgf Y ((prodMkLeft Ω' η) ω') t ≤ rexp (↑cY * t ^ 2 / 2)
Module: Mathlib.Probability.Moments.SubGaussian

Goal state before:
pp: Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
inst✝¹ : SFinite ν
η : Kernel (Ω' × Ω) Ω''
inst✝ : IsZeroOrMarkovKernel η
hX : HasSubgaussianMGF X c κ ν
hY : HasSubgaussianMGF Y cY η (ν ⊗ₘ κ)
t : ℝ
hκ : IsSFiniteKernel κ
hη : IsMarkovKernel η
h : MemLp (fun ω ↦ rexp (t * X ω)) 2 (⇑κ ∘ₘ ν)
⊢ ⇑κ ∘ₘ ν = ⇑κ ∘ₘ ν

Tactic:
]

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

Goal state before:
pp: ι : Type u_1
Ω : Type u_2
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁵ : ConditionallyCompleteLinearOrderBot ι
inst✝⁴ : TopologicalSpace ι
inst✝³ : OrderTopology ι
𝓕 : Filtration ι mΩ
inst✝² : SecondCountableTopology ι
inst✝¹ : IsFiniteMeasure P
inst✝ : NoMaxOrder ι
τ : ℕ → Ω → WithTop ι
σ : ℕ → ℕ → Ω → WithTop ι
hτ : IsLocalizingSequence 𝓕 τ P
hσ : ∀ (n : ℕ), IsLocalizingSequence 𝓕 (σ n) P
⊢ ∃ nk, StrictMono nk ∧ IsPreLocalizingSequence 𝓕 (fun i ω ↦ min (τ i ω) (σ i (nk i) ω)) P

Tactic:
by
  obtain ⟨nk, T, hnk, hT, hP⟩ := isPreLocalizingSequence_of_isLocalizingSequence_aux hτ hσ
  refine ⟨nk, hnk, fun n ↦ (hτ.isStoppingTime n).min ((hσ _).isStoppingTime _), ?_⟩
  have : ∑' n, P {ω | σ n (nk n) ω < min (τ n ω) (T n)} < ∞ :=
    lt_of_le_of_lt (ENNReal.summable.tsum_mono ENNReal.summable hP)
      (tsum_geometric_lt_top.2 <| by simp)
  filter_upwards [ae_eventually_notMem this.ne, hτ.tendsto_top] with ω hω hωτ
  exact hωτ.min <| tendsto_of_tendsto_of_tendsto_of_le_of_le'
    (hωτ.min <| WithTop.tendsto_coe_atTop.comp hT) tendsto_const_nhds (by grind) (by simp)

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

Goal state before:
pp: case pos.inr.refine_1
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
inst✝¹ : SFinite ν
η : Kernel (Ω' × Ω) Ω''
inst✝ : IsZeroOrMarkovKernel η
hX : HasSubgaussianMGF X c κ ν
hY : HasSubgaussianMGF Y cY η (ν ⊗ₘ κ)
t : ℝ
hκ : IsSFiniteKernel κ
hη : IsMarkovKernel η
h : MemLp ((fun ω ↦ rexp (t * X ω)) ∘ Prod.fst) 2 (⇑(κ ⊗ₖ η) ∘ₘ ν)
this : ⇑κ ∘ₘ ν = Measure.map Prod.fst (⇑(κ ⊗ₖ η) ∘ₘ ν)
⊢ MemLp (fun ω ↦ rexp (t * X ω.1)) 2 (⇑(κ ⊗ₖ η) ∘ₘ ν)

Tactic:
]

Goal state after:
case pos.inr.refine_1
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
inst✝¹ : SFinite ν
η : Kernel (Ω' × Ω) Ω''
inst✝ : IsZeroOrMarkovKernel η
hX : HasSubgaussianMGF X c κ ν
hY : HasSubgaussianMGF Y cY η (ν ⊗ₘ κ)
t : ℝ
hκ : IsSFiniteKernel κ
hη : IsMarkovKernel η
h : MemLp ((fun ω ↦ rexp (t * X ω)) ∘ Prod.fst) 2 (⇑(κ ⊗ₖ η) ∘ₘ ν)
this : ⇑κ ∘ₘ ν = Measure.map Prod.fst (⇑(κ ⊗ₖ η) ∘ₘ ν)
⊢ MemLp (fun ω ↦ rexp (t * X ω.1)) 2 (⇑(κ ⊗ₖ η) ∘ₘ ν)
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ ∫ (x : ℝ) in ↑0..↑N, x + 1 ∂ρ = ∫ (x : ℝ) in 0..↑N, x + 1 ∂ρ

pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
k : ℕ
x✝ : k < N
⊢ IntervalIntegrable (fun x ↦ x + 1) ρ ↑k ↑(k + 1)

Tactic:
]

Goal state after:
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ ∫ (x : ℝ) in ↑0..↑N, x + 1 ∂ρ = ∫ (x : ℝ) in 0..↑N, x + 1 ∂ρ

Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
k : ℕ
x✝ : k < N
⊢ IntervalIntegrable (fun x ↦ x + 1) ρ ↑k ↑(k + 1)
Module: Mathlib.Probability.Moments.SubGaussian

Goal state before:
pp: case pos.inr.refine_2
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
inst✝¹ : SFinite ν
η : Kernel (Ω' × Ω) Ω''
inst✝ : IsZeroOrMarkovKernel η
hX : HasSubgaussianMGF X c κ ν
hY : HasSubgaussianMGF Y cY η (ν ⊗ₘ κ)
t : ℝ
hκ : IsSFiniteKernel κ
hη : IsMarkovKernel η
h : MemLp ((fun ω ↦ rexp (t * Y ω)) ∘ Prod.snd) 2 (⇑(κ ⊗ₖ η) ∘ₘ ν)
⊢ MemLp (fun ω ↦ rexp (t * Y ω.2)) 2 (⇑(κ ⊗ₖ η) ∘ₘ ν)

Tactic:
]

Goal state after:
case pos.inr.refine_2
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
inst✝¹ : SFinite ν
η : Kernel (Ω' × Ω) Ω''
inst✝ : IsZeroOrMarkovKernel η
hX : HasSubgaussianMGF X c κ ν
hY : HasSubgaussianMGF Y cY η (ν ⊗ₘ κ)
t : ℝ
hκ : IsSFiniteKernel κ
hη : IsMarkovKernel η
h : MemLp ((fun ω ↦ rexp (t * Y ω)) ∘ Prod.snd) 2 (⇑(κ ⊗ₖ η) ∘ₘ ν)
⊢ MemLp (fun ω ↦ rexp (t * Y ω.2)) 2 (⇑(κ ⊗ₖ η) ∘ₘ ν)
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ ∫ (x : ℝ) in 0..↑N, x ∂ρ + ∫ (x : ℝ) in 0..↑N, 1 ∂ρ = ∫ (x : ℝ) in 0..↑N, x ∂ρ + ∫ (x : ℝ) in 0..↑N, 1 ∂ρ

pp: case hf
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ IntervalIntegrable (fun x ↦ x) ρ 0 ↑N

pp: case hg
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ IntervalIntegrable (fun x ↦ 1) ρ 0 ↑N

Tactic:
]

Goal state after:
case hf
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ IntervalIntegrable (fun x ↦ x) ρ 0 ↑N

case hg
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ IntervalIntegrable (fun x ↦ 1) ρ 0 ↑N
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ ∫ (x : ℝ) in 0..↑N, x ∂ρ + ∫ (x : ℝ) in 0..↑N, 1 ∂ρ =
    ∫ (y : ℝ) in 0..↑N, y ∂Measure.map X ℙ + ∫ (x : ℝ) in 0..↑N, 1 ∂ρ

Tactic:
]

Goal state after:
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ (∫ (x : Ω), X x) + ∫ (x : ℝ) in 0..↑N, 1 ∂ρ ≤ (∫ (a : Ω), X a) + ∫ (x : ℝ) in 0..↑N, 1 ∂ρ

Tactic:
]

Goal state after:
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: case h₂
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ ∫ (x : ℝ) in Set.Ioc 0 ↑N, 1 ∂ρ ≤ 1

Tactic:
]

Goal state after:
case h₂
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ ∫ (x : ℝ) in Set.Ioc 0 ↑N, 1 ∂ρ ≤ 1
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: case h₂
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ ρ.real (Set.Ioc 0 ↑N) ≤ ENNReal.toReal 1

Tactic:
]

Goal state after:
case h₂
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
⊢ ρ.real (Set.Ioc 0 ↑N) ≤ ENNReal.toReal 1
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
A : ∑ j ∈ range K, ∫ (x : ℝ) in ↑j..↑N, 1 ∂ρ ≤ (∫ (a : Ω), X a) + 1
a b : ℝ
⊢ ℙ {ω | X ω ∈ Set.Ioc a b} = ℙ (X ⁻¹' Set.Ioc a b)

Tactic:
]

Goal state after:
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
A : ∑ j ∈ range K, ∫ (x : ℝ) in ↑j..↑N, 1 ∂ρ ≤ (∫ (a : Ω), X a) + 1
a b : ℝ
⊢ ℙ {ω | X ω ∈ Set.Ioc a b} = ℙ (X ⁻¹' Set.Ioc a b)
Module: Mathlib.Probability.Process.LocalProperty

Goal state before:
pp: ι : Type u_1
Ω : Type u_2
E : Type u_3
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁸ : ConditionallyCompleteLinearOrderBot ι
inst✝⁷ : TopologicalSpace ι
inst✝⁶ : OrderTopology ι
𝓕 : Filtration ι mΩ
X : ι → Ω → E
p : (ι → Ω → E) → Prop
inst✝⁵ : SecondCountableTopology ι
inst✝⁴ : IsFiniteMeasure P
inst✝³ : DenselyOrdered ι
inst✝² : NoMaxOrder ι
inst✝¹ : Zero E
inst✝ : 𝓕.IsRightContinuous
hp : IsStable 𝓕 p
hL : Locally (fun Y ↦ Locally p 𝓕 Y P) 𝓕 X P
τ : ℕ → ℕ → Ω → WithTop ι
hτ₁ : ∀ (n : ℕ), IsLocalizingSequence 𝓕 (τ n) P
hτ₂ :
  ∀ (n n_1 : ℕ),
    p
      (stoppedProcess
        (fun i ↦
          {ω | ⊥ < τ n n_1 ω}.indicator
            (stoppedProcess (fun i ↦ {ω | ⊥ < hL.localSeq n ω}.indicator (X i)) (hL.localSeq n) i))
        (τ n n_1))
nk : ℕ → ℕ
hnk : StrictMono nk
hpre : IsPreLocalizingSequence 𝓕 (fun i ω ↦ min (hL.localSeq i ω) (τ i (nk i) ω)) P
n : ℕ
i : ι
ω : Ω
⊢ (fun i ↦
        {ω | ⊥ < min (hL.localSeq n ω) (τ n (nk n) ω)}.indicator
          (stoppedProcess X (fun ω ↦ min (hL.localSeq n ω) (τ n (nk n) ω)) i))
      i ω =
    (fun i ↦
        {ω | ⊥ < τ n (nk n) ω}.indicator
          ((fun i ↦ {ω | ⊥ < hL.localSeq n ω}.indicator (stoppedProcess X (τ n (nk n) ⊓ hL.localSeq n) i)) i))
      i ω

Tactic:
]

Goal state after:
ι : Type u_1
Ω : Type u_2
E : Type u_3
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁸ : ConditionallyCompleteLinearOrderBot ι
inst✝⁷ : TopologicalSpace ι
inst✝⁶ : OrderTopology ι
𝓕 : Filtration ι mΩ
X : ι → Ω → E
p : (ι → Ω → E) → Prop
inst✝⁵ : SecondCountableTopology ι
inst✝⁴ : IsFiniteMeasure P
inst✝³ : DenselyOrdered ι
inst✝² : NoMaxOrder ι
inst✝¹ : Zero E
inst✝ : 𝓕.IsRightContinuous
hp : IsStable 𝓕 p
hL : Locally (fun Y ↦ Locally p 𝓕 Y P) 𝓕 X P
τ : ℕ → ℕ → Ω → WithTop ι
hτ₁ : ∀ (n : ℕ), IsLocalizingSequence 𝓕 (τ n) P
hτ₂ :
  ∀ (n n_1 : ℕ),
    p
      (stoppedProcess
        (fun i ↦
          {ω | ⊥ < τ n n_1 ω}.indicator
            (stoppedProcess (fun i ↦ {ω | ⊥ < hL.localSeq n ω}.indicator (X i)) (hL.localSeq n) i))
        (τ n n_1))
nk : ℕ → ℕ
hnk : StrictMono nk
hpre : IsPreLocalizingSequence 𝓕 (fun i ω ↦ min (hL.localSeq i ω) (τ i (nk i) ω)) P
n : ℕ
i : ι
ω : Ω
⊢ (fun i ↦
        {ω | ⊥ < min (hL.localSeq n ω) (τ n (nk n) ω)}.indicator
          (stoppedProcess X (fun ω ↦ min (hL.localSeq n ω) (τ n (nk n) ω)) i))
      i ω =
    (fun i ↦
        {ω | ⊥ < τ n (nk n) ω}.indicator
          ((fun i ↦ {ω | ⊥ < hL.localSeq n ω}.indicator (stoppedProcess X (τ n (nk n) ⊓ hL.localSeq n) i)) i))
      i ω
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := Measure.map X ℙ
this : IsProbabilityMeasure ρ
A : ∑ j ∈ range K, ∫ (x : ℝ) in ↑j..↑N, 1 ∂ρ ≤ (∫ (a : Ω), X a) + 1
B : ∀ (a b : ℝ), ℙ {ω | X ω ∈ Set.Ioc a b} = ENNReal.ofReal (∫ (x : ℝ) in Set.Ioc a b, 1 ∂ρ)
j : ℕ
hj : j ∈ range K
⊢ ∫ (x : ℝ) in Set.Ioc ↑j ↑N, 1 ∂ρ = ∫ (x : ℝ) in Set.Ioc ↑j ↑N, 1 ∂ρ

Tactic:
]

Goal state after:
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K i : ℕ
x✝ : i ∈ range K
this : {ω | X ω ∈ Set.Ioi ↑i} = ⋃ N, {ω | X ω ∈ Set.Ioc ↑i ↑N}
⊢ Tendsto (fun N ↦ ℙ {ω | X ω ∈ Set.Ioc ↑i ↑N}) atTop (𝓝 (ℙ (⋃ N, {ω | X ω ∈ Set.Ioc ↑i ↑N})))

Tactic:
]

Goal state after:
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K i : ℕ
x✝ : i ∈ range K
this : {ω | X ω ∈ Set.Ioi ↑i} = ⋃ N, {ω | X ω ∈ Set.Ioc ↑i ↑N}
⊢ Tendsto (fun N ↦ ℙ {ω | X ω ∈ Set.Ioc ↑i ↑N}) atTop (𝓝 (ℙ (⋃ N, {ω | X ω ∈ Set.Ioc ↑i ↑N})))
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K : ℕ
Y : ℕ → Ω → ℝ := fun n ↦ truncation X ↑n
ρ : Measure ℝ := Measure.map X ℙ
n : ℕ
⊢ ∫ (y : ℝ) in 0..↑n, y ^ 2 ∂Measure.map X ℙ = ∫ (x : ℝ) in 0..↑n, x ^ 2 ∂ρ

Tactic:
]

Goal state after:
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: case e_f.e_a
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K : ℕ
Y : ℕ → Ω → ℝ := fun n ↦ truncation X ↑n
ρ : Measure ℝ := Measure.map X ℙ
Y2 : ∀ (n : ℕ), ∫ (a : Ω), (Y n ^ 2) a = ∫ (x : ℝ) in 0..↑n, x ^ 2 ∂ρ
j : ℕ
⊢ ∫ (x : ℝ) in 0..↑j, x ^ 2 ∂ρ = ∫ (x : ℝ) in ↑0..↑j, x ^ 2 ∂ρ

pp: case e_f.e_a
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K : ℕ
Y : ℕ → Ω → ℝ := fun n ↦ truncation X ↑n
ρ : Measure ℝ := Measure.map X ℙ
Y2 : ∀ (n : ℕ), ∫ (a : Ω), (Y n ^ 2) a = ∫ (x : ℝ) in 0..↑n, x ^ 2 ∂ρ
j : ℕ
⊢ ∀ k < j, IntervalIntegrable (fun x ↦ x ^ 2) ρ ↑k ↑(k + 1)

Tactic:
]

Goal state after:
case e_f.e_a
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K : ℕ
Y : ℕ → Ω → ℝ := fun n ↦ truncation X ↑n
ρ : Measure ℝ := Measure.map X ℙ
Y2 : ∀ (n : ℕ), ∫ (a : Ω), (Y n ^ 2) a = ∫ (x : ℝ) in 0..↑n, x ^ 2 ∂ρ
j : ℕ
⊢ ∫ (x : ℝ) in 0..↑j, x ^ 2 ∂ρ = ∫ (x : ℝ) in ↑0..↑j, x ^ 2 ∂ρ

case e_f.e_a
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K : ℕ
Y : ℕ → Ω → ℝ := fun n ↦ truncation X ↑n
ρ : Measure ℝ := Measure.map X ℙ
Y2 : ∀ (n : ℕ), ∫ (a : Ω), (Y n ^ 2) a = ∫ (x : ℝ) in 0..↑n, x ^ 2 ∂ρ
j : ℕ
⊢ ∀ k < j, IntervalIntegrable (fun x ↦ x ^ 2) ρ ↑k ↑(k + 1)
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: case h
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K : ℕ
Y : ℕ → Ω → ℝ := fun n ↦ truncation X ↑n
ρ : Measure ℝ := Measure.map X ℙ
Y2 : ∀ (n : ℕ), ∫ (a : Ω), (Y n ^ 2) a = ∫ (x : ℝ) in 0..↑n, x ^ 2 ∂ρ
k : ℕ
a✝ : k ∈ range K
Ik : ↑k ≤ ↑(k + 1)
⊢ ∫ (x : ℝ) in Set.Ioc ↑k ↑(k + 1), 2 / (↑k + 1) * x ^ 2 ∂ρ ≤ ∫ (x : ℝ) in Set.Ioc ↑k ↑(k + 1), 2 * x ∂ρ

Tactic:
]

Goal state after:
case h
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K : ℕ
Y : ℕ → Ω → ℝ := fun n ↦ truncation X ↑n
ρ : Measure ℝ := Measure.map X ℙ
Y2 : ∀ (n : ℕ), ∫ (a : Ω), (Y n ^ 2) a = ∫ (x : ℝ) in 0..↑n, x ^ 2 ∂ρ
k : ℕ
a✝ : k ∈ range K
Ik : ↑k ≤ ↑(k + 1)
⊢ ∫ (x : ℝ) in Set.Ioc ↑k ↑(k + 1), 2 / (↑k + 1) * x ^ 2 ∂ρ ≤ ∫ (x : ℝ) in Set.Ioc ↑k ↑(k + 1), 2 * x ∂ρ