mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Independence

Goal state before:
pp: case refine_2.hX
T : Type u_1
Ω : Type u_2
E : Type u_3
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : MeasurableSpace E
inst✝³ : BorelSpace E
inst✝² : SecondCountableTopology E
inst✝¹ : CompleteSpace E
S : Type u_4
X : S → Ω → E
Y : T → Ω → E
inst✝ : NormedSpace ℝ E
hXY : IsGaussianProcess (Sum.elim X Y) P
mX : ∀ (s : S), AEMeasurable (X s) P
mY : ∀ (t : T), AEMeasurable (Y t) P
h : ∀ (s : S) (t : T) (L₁ L₂ : StrongDual ℝ E), cov[⇑L₁ ∘ X s, ⇑L₂ ∘ Y t; P] = 0
this : IsProbabilityMeasure P
I : Finset S
J : Finset T
L₁ : StrongDual ℝ (↥I → E)
L₂ : StrongDual ℝ (↥J → E)
h1 : (⇑L₁ ∘ fun ω i ↦ X (↑i) ω) = ∑ i, ⇑(L₁ ∘SL ContinuousLinearMap.single ℝ (fun i ↦ E) i) ∘ X ↑i
h2 : (⇑L₂ ∘ fun ω j ↦ Y (↑j) ω) = ∑ j, ⇑(L₂ ∘SL ContinuousLinearMap.single ℝ (fun j ↦ E) j) ∘ Y ↑j
⊢ ∀ (i : ↥I), MemLp (⇑(L₁ ∘SL ContinuousLinearMap.single ℝ (fun i ↦ E) i) ∘ X ↑i) 2 P

pp: case refine_2.hY
T : Type u_1
Ω : Type u_2
E : Type u_3
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : MeasurableSpace E
inst✝³ : BorelSpace E
inst✝² : SecondCountableTopology E
inst✝¹ : CompleteSpace E
S : Type u_4
X : S → Ω → E
Y : T → Ω → E
inst✝ : NormedSpace ℝ E
hXY : IsGaussianProcess (Sum.elim X Y) P
mX : ∀ (s : S), AEMeasurable (X s) P
mY : ∀ (t : T), AEMeasurable (Y t) P
h : ∀ (s : S) (t : T) (L₁ L₂ : StrongDual ℝ E), cov[⇑L₁ ∘ X s, ⇑L₂ ∘ Y t; P] = 0
this : IsProbabilityMeasure P
I : Finset S
J : Finset T
L₁ : StrongDual ℝ (↥I → E)
L₂ : StrongDual ℝ (↥J → E)
h1 : (⇑L₁ ∘ fun ω i ↦ X (↑i) ω) = ∑ i, ⇑(L₁ ∘SL ContinuousLinearMap.single ℝ (fun i ↦ E) i) ∘ X ↑i
h2 : (⇑L₂ ∘ fun ω j ↦ Y (↑j) ω) = ∑ j, ⇑(L₂ ∘SL ContinuousLinearMap.single ℝ (fun j ↦ E) j) ∘ Y ↑j
⊢ ∀ (i : ↥J), MemLp (⇑(L₂ ∘SL ContinuousLinearMap.single ℝ (fun j ↦ E) i) ∘ Y ↑i) 2 P

Tactic:
·

Goal state after:
case refine_2.hX
T : Type u_1
Ω : Type u_2
E : Type u_3
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : MeasurableSpace E
inst✝³ : BorelSpace E
inst✝² : SecondCountableTopology E
inst✝¹ : CompleteSpace E
S : Type u_4
X : S → Ω → E
Y : T → Ω → E
inst✝ : NormedSpace ℝ E
hXY : IsGaussianProcess (Sum.elim X Y) P
mX : ∀ (s : S), AEMeasurable (X s) P
mY : ∀ (t : T), AEMeasurable (Y t) P
h : ∀ (s : S) (t : T) (L₁ L₂ : StrongDual ℝ E), cov[⇑L₁ ∘ X s, ⇑L₂ ∘ Y t; P] = 0
this : IsProbabilityMeasure P
I : Finset S
J : Finset T
L₁ : StrongDual ℝ (↥I → E)
L₂ : StrongDual ℝ (↥J → E)
h1 : (⇑L₁ ∘ fun ω i ↦ X (↑i) ω) = ∑ i, ⇑(L₁ ∘SL ContinuousLinearMap.single ℝ (fun i ↦ E) i) ∘ X ↑i
h2 : (⇑L₂ ∘ fun ω j ↦ Y (↑j) ω) = ∑ j, ⇑(L₂ ∘SL ContinuousLinearMap.single ℝ (fun j ↦ E) j) ∘ Y ↑j
⊢ ∀ (i : ↥I), MemLp (⇑(L₁ ∘SL ContinuousLinearMap.single ℝ (fun i ↦ E) i) ∘ X ↑i) 2 P
Module: Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Independence

Goal state before:
pp: case refine_2.hY
T : Type u_1
Ω : Type u_2
E : Type u_3
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : MeasurableSpace E
inst✝³ : BorelSpace E
inst✝² : SecondCountableTopology E
inst✝¹ : CompleteSpace E
S : Type u_4
X : S → Ω → E
Y : T → Ω → E
inst✝ : NormedSpace ℝ E
hXY : IsGaussianProcess (Sum.elim X Y) P
mX : ∀ (s : S), AEMeasurable (X s) P
mY : ∀ (t : T), AEMeasurable (Y t) P
h : ∀ (s : S) (t : T) (L₁ L₂ : StrongDual ℝ E), cov[⇑L₁ ∘ X s, ⇑L₂ ∘ Y t; P] = 0
this : IsProbabilityMeasure P
I : Finset S
J : Finset T
L₁ : StrongDual ℝ (↥I → E)
L₂ : StrongDual ℝ (↥J → E)
h1 : (⇑L₁ ∘ fun ω i ↦ X (↑i) ω) = ∑ i, ⇑(L₁ ∘SL ContinuousLinearMap.single ℝ (fun i ↦ E) i) ∘ X ↑i
h2 : (⇑L₂ ∘ fun ω j ↦ Y (↑j) ω) = ∑ j, ⇑(L₂ ∘SL ContinuousLinearMap.single ℝ (fun j ↦ E) j) ∘ Y ↑j
⊢ ∀ (i : ↥J), MemLp (⇑(L₂ ∘SL ContinuousLinearMap.single ℝ (fun j ↦ E) i) ∘ Y ↑i) 2 P

Tactic:
·

Goal state after:
case refine_2.hY
T : Type u_1
Ω : Type u_2
E : Type u_3
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : MeasurableSpace E
inst✝³ : BorelSpace E
inst✝² : SecondCountableTopology E
inst✝¹ : CompleteSpace E
S : Type u_4
X : S → Ω → E
Y : T → Ω → E
inst✝ : NormedSpace ℝ E
hXY : IsGaussianProcess (Sum.elim X Y) P
mX : ∀ (s : S), AEMeasurable (X s) P
mY : ∀ (t : T), AEMeasurable (Y t) P
h : ∀ (s : S) (t : T) (L₁ L₂ : StrongDual ℝ E), cov[⇑L₁ ∘ X s, ⇑L₂ ∘ Y t; P] = 0
this : IsProbabilityMeasure P
I : Finset S
J : Finset T
L₁ : StrongDual ℝ (↥I → E)
L₂ : StrongDual ℝ (↥J → E)
h1 : (⇑L₁ ∘ fun ω i ↦ X (↑i) ω) = ∑ i, ⇑(L₁ ∘SL ContinuousLinearMap.single ℝ (fun i ↦ E) i) ∘ X ↑i
h2 : (⇑L₂ ∘ fun ω j ↦ Y (↑j) ω) = ∑ j, ⇑(L₂ ∘SL ContinuousLinearMap.single ℝ (fun j ↦ E) j) ∘ Y ↑j
⊢ ∀ (i : ↥J), MemLp (⇑(L₂ ∘SL ContinuousLinearMap.single ℝ (fun j ↦ E) i) ∘ Y ↑i) 2 P
Module: Mathlib.Probability.Distributions.Fernique

Goal state before:
pp: case pos
E : Type u_1
inst✝⁵ : SeminormedAddCommGroup E
inst✝⁴ : NormedSpace ℝ E
inst✝³ : SecondCountableTopology E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsFiniteMeasure μ
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
hμ_zero : μ = 0
⊢ ∃ C, 0 < C ∧ Integrable (fun x ↦ rexp (C * ‖x‖ ^ 2)) μ

pp: case neg
E : Type u_1
inst✝⁵ : SeminormedAddCommGroup E
inst✝⁴ : NormedSpace ℝ E
inst✝³ : SecondCountableTopology E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsFiniteMeasure μ
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
hμ_zero : ¬μ = 0
⊢ ∃ C, 0 < C ∧ Integrable (fun x ↦ rexp (C * ‖x‖ ^ 2)) μ

Tactic:
·

Goal state after:
case pos
E : Type u_1
inst✝⁵ : SeminormedAddCommGroup E
inst✝⁴ : NormedSpace ℝ E
inst✝³ : SecondCountableTopology E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsFiniteMeasure μ
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
hμ_zero : μ = 0
⊢ ∃ C, 0 < C ∧ Integrable (fun x ↦ rexp (C * ‖x‖ ^ 2)) μ
Module: Mathlib.Probability.Independence.Process.HasIndepIncrements.IsGaussianProcess

Goal state before:
pp: case inl
T : Type u_1
inst✝¹ : LinearOrder T
I : Finset T
inst✝ : OrderBot T
j : Fin (#I + 1)
hij : 0 ≤ j
⊢ I.orderEmbOfFinWithBot 0 ≤ I.orderEmbOfFinWithBot j

pp: case inr
T : Type u_1
inst✝¹ : LinearOrder T
I : Finset T
inst✝ : OrderBot T
i j : Fin (#I + 1)
hij : i ≤ j
hi : i ≠ 0
⊢ I.orderEmbOfFinWithBot i ≤ I.orderEmbOfFinWithBot j

Tactic:
·

Goal state after:
case inl
T : Type u_1
inst✝¹ : LinearOrder T
I : Finset T
inst✝ : OrderBot T
j : Fin (#I + 1)
hij : 0 ≤ j
⊢ I.orderEmbOfFinWithBot 0 ≤ I.orderEmbOfFinWithBot j
Module: Mathlib.Probability.Independence.Process.Basic

Goal state before:
pp: case inl
S : Type u_1
Ω : Type u_3
mΩ : MeasurableSpace Ω
α : Type u_4
mα : MeasurableSpace α
κ : Kernel α Ω
T : S → Type u_5
𝓧 : (i : S) → T i → Type u_6
inst✝ : (i : S) → (j : T i) → MeasurableSpace (𝓧 i j)
X : (i : S) → (j : T i) → Ω → 𝓧 i j
hX : ∀ (i : S) (j : T i), Measurable (X i j)
h : ∀ (I : Finset S) (J : (i : ↥I) → Finset (T ↑i)), iIndepFun (fun i ω j ↦ X (↑i) (↑j) ω) κ 0
⊢ iIndepFun (fun i ω j ↦ X i j ω) κ 0

pp: case inr
S : Type u_1
Ω : Type u_3
mΩ : MeasurableSpace Ω
α : Type u_4
mα : MeasurableSpace α
κ : Kernel α Ω
P : Measure α
T : S → Type u_5
𝓧 : (i : S) → T i → Type u_6
inst✝ : (i : S) → (j : T i) → MeasurableSpace (𝓧 i j)
X : (i : S) → (j : T i) → Ω → 𝓧 i j
hX : ∀ (i : S) (j : T i), Measurable (X i j)
h : ∀ (I : Finset S) (J : (i : ↥I) → Finset (T ↑i)), iIndepFun (fun i ω j ↦ X (↑i) (↑j) ω) κ P
hμ : P ≠ 0
⊢ iIndepFun (fun i ω j ↦ X i j ω) κ P

Tactic:
·

Goal state after:
case inl
S : Type u_1
Ω : Type u_3
mΩ : MeasurableSpace Ω
α : Type u_4
mα : MeasurableSpace α
κ : Kernel α Ω
T : S → Type u_5
𝓧 : (i : S) → T i → Type u_6
inst✝ : (i : S) → (j : T i) → MeasurableSpace (𝓧 i j)
X : (i : S) → (j : T i) → Ω → 𝓧 i j
hX : ∀ (i : S) (j : T i), Measurable (X i j)
h : ∀ (I : Finset S) (J : (i : ↥I) → Finset (T ↑i)), iIndepFun (fun i ω j ↦ X (↑i) (↑j) ω) κ 0
⊢ iIndepFun (fun i ω j ↦ X i j ω) κ 0
Module: Mathlib.Probability.Independence.Process.HasIndepIncrements.IsGaussianProcess

Goal state before:
pp: case inl
T : Type u_1
Ω : Type u_2
E : Type u_3
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁷ : LinearOrder T
inst✝⁶ : OrderBot T
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : NormedSpace ℝ E
inst✝³ : MeasurableSpace E
inst✝² : BorelSpace E
inst✝¹ : SecondCountableTopology E
inst✝ : CompleteSpace E
X : T → Ω → E
law : ∀ (t : T), HasGaussianLaw (X t) P
h_bot : ∀ᵐ (ω : Ω) ∂P, X ⊥ ω = 0
incr : HasIndepIncrements X P
this : IsProbabilityMeasure P
⊢ HasGaussianLaw (fun ω ↦ ∅.restrict fun x ↦ X x ω) P

pp: case inr
T : Type u_1
Ω : Type u_2
E : Type u_3
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁷ : LinearOrder T
inst✝⁶ : OrderBot T
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : NormedSpace ℝ E
inst✝³ : MeasurableSpace E
inst✝² : BorelSpace E
inst✝¹ : SecondCountableTopology E
inst✝ : CompleteSpace E
X : T → Ω → E
law : ∀ (t : T), HasGaussianLaw (X t) P
h_bot : ∀ᵐ (ω : Ω) ∂P, X ⊥ ω = 0
incr : HasIndepIncrements X P
I : Finset T
this : IsProbabilityMeasure P
hI : I.Nonempty
⊢ HasGaussianLaw (fun ω ↦ I.restrict fun x ↦ X x ω) P

Tactic:
·

Goal state after:
case inl
T : Type u_1
Ω : Type u_2
E : Type u_3
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁷ : LinearOrder T
inst✝⁶ : OrderBot T
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : NormedSpace ℝ E
inst✝³ : MeasurableSpace E
inst✝² : BorelSpace E
inst✝¹ : SecondCountableTopology E
inst✝ : CompleteSpace E
X : T → Ω → E
law : ∀ (t : T), HasGaussianLaw (X t) P
h_bot : ∀ᵐ (ω : Ω) ∂P, X ⊥ ω = 0
incr : HasIndepIncrements X P
this : IsProbabilityMeasure P
⊢ HasGaussianLaw (fun ω ↦ ∅.restrict fun x ↦ X x ω) P
Module: Mathlib.Probability.Independence.Process.Basic

Goal state before:
pp: case inr
S : Type u_1
Ω : Type u_3
mΩ : MeasurableSpace Ω
α : Type u_4
mα : MeasurableSpace α
κ : Kernel α Ω
P : Measure α
T : S → Type u_5
𝓧 : (i : S) → T i → Type u_6
inst✝ : (i : S) → (j : T i) → MeasurableSpace (𝓧 i j)
X : (i : S) → (j : T i) → Ω → 𝓧 i j
hX : ∀ (i : S) (j : T i), Measurable (X i j)
h : ∀ (I : Finset S) (J : (i : ↥I) → Finset (T ↑i)), iIndepFun (fun i ω j ↦ X (↑i) (↑j) ω) κ P
hμ : P ≠ 0
η : Kernel α Ω
η_eq : ⇑κ =ᵐ[P] ⇑η
hη : IsMarkovKernel η
π : S → Set (Set Ω) := fun i ↦ {s | ∃ t ∈ squareCylinders fun j ↦ {s | MeasurableSet s}, (fun ω j ↦ X i j ω) ⁻¹' t = s}
π_pi : ∀ (i : S), IsPiSystem (π i)
π_gen : ∀ (i : S), MeasurableSpace.comap (fun ω j ↦ X i j ω) MeasurableSpace.pi = generateFrom (π i)
I : Finset S
s : S → Set Ω
J : (i : S) → Finset (T i)
t : (i : S) → (i_1 : T i) → Set (𝓧 i i_1)
ht : ∀ i ∈ I, ∀ (i_1 : T i), MeasurableSet (t i i_1)
hs : ∀ i ∈ I, (fun ω j ↦ X i j ω) ⁻¹' (↑(J i)).pi (t i) = s i
⊢ ∀ᵐ (a : α) ∂P, (η a) (⋂ i ∈ I, s i) = ∏ i ∈ I, (η a) (s i)

Tactic:
Set.iInter₂_congr (fun i hi ↦ (hs i hi).symm),

Goal state after:
case inr
S : Type u_1
Ω : Type u_3
mΩ : MeasurableSpace Ω
α : Type u_4
mα : MeasurableSpace α
κ : Kernel α Ω
P : Measure α
T : S → Type u_5
𝓧 : (i : S) → T i → Type u_6
inst✝ : (i : S) → (j : T i) → MeasurableSpace (𝓧 i j)
X : (i : S) → (j : T i) → Ω → 𝓧 i j
hX : ∀ (i : S) (j : T i), Measurable (X i j)
h : ∀ (I : Finset S) (J : (i : ↥I) → Finset (T ↑i)), iIndepFun (fun i ω j ↦ X (↑i) (↑j) ω) κ P
hμ : P ≠ 0
η : Kernel α Ω
η_eq : ⇑κ =ᵐ[P] ⇑η
hη : IsMarkovKernel η
π : S → Set (Set Ω) := fun i ↦ {s | ∃ t ∈ squareCylinders fun j ↦ {s | MeasurableSet s}, (fun ω j ↦ X i j ω) ⁻¹' t = s}
π_pi : ∀ (i : S), IsPiSystem (π i)
π_gen : ∀ (i : S), MeasurableSpace.comap (fun ω j ↦ X i j ω) MeasurableSpace.pi = generateFrom (π i)
I : Finset S
s : S → Set Ω
J : (i : S) → Finset (T i)
t : (i : S) → (i_1 : T i) → Set (𝓧 i i_1)
ht : ∀ i ∈ I, ∀ (i_1 : T i), MeasurableSet (t i i_1)
hs : ∀ i ∈ I, (fun ω j ↦ X i j ω) ⁻¹' (↑(J i)).pi (t i) = s i
⊢ ∀ᵐ (a : α) ∂P, (η a) (⋂ i ∈ I, (fun ω j ↦ X i j ω) ⁻¹' (↑(J i)).pi (t i)) = ∏ i ∈ I, (η a) (s i)
Module: Mathlib.Probability.Distributions.Fernique

Goal state before:
pp: case neg.h₁
E : Type u_1
inst✝⁵ : SeminormedAddCommGroup E
inst✝⁴ : NormedSpace ℝ E
inst✝³ : SecondCountableTopology E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsFiniteMeasure μ
h_rot✝ : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
hμ_zero : ¬μ = 0
μ' : Measure E := μ[|Set.univ]
hμ'_eq : μ' = (μ Set.univ)⁻¹ • μ
hμ' : IsProbabilityMeasure μ'
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ'.prod μ') = μ'.prod μ'
C : ℝ
hC_pos : 0 < C
hC : Integrable (fun x ↦ rexp (C * ‖x‖ ^ 2)) ((μ Set.univ)⁻¹ • μ)
⊢ (μ Set.univ)⁻¹ ≠ 0

pp: case neg.h₂
E : Type u_1
inst✝⁵ : SeminormedAddCommGroup E
inst✝⁴ : NormedSpace ℝ E
inst✝³ : SecondCountableTopology E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsFiniteMeasure μ
h_rot✝ : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
hμ_zero : ¬μ = 0
μ' : Measure E := μ[|Set.univ]
hμ'_eq : μ' = (μ Set.univ)⁻¹ • μ
hμ' : IsProbabilityMeasure μ'
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ'.prod μ') = μ'.prod μ'
C : ℝ
hC_pos : 0 < C
hC : Integrable (fun x ↦ rexp (C * ‖x‖ ^ 2)) ((μ Set.univ)⁻¹ • μ)
⊢ (μ Set.univ)⁻¹ ≠ ∞

Tactic:
·

Goal state after:
case neg.h₁
E : Type u_1
inst✝⁵ : SeminormedAddCommGroup E
inst✝⁴ : NormedSpace ℝ E
inst✝³ : SecondCountableTopology E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsFiniteMeasure μ
h_rot✝ : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
hμ_zero : ¬μ = 0
μ' : Measure E := μ[|Set.univ]
hμ'_eq : μ' = (μ Set.univ)⁻¹ • μ
hμ' : IsProbabilityMeasure μ'
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ'.prod μ') = μ'.prod μ'
C : ℝ
hC_pos : 0 < C
hC : Integrable (fun x ↦ rexp (C * ‖x‖ ^ 2)) ((μ Set.univ)⁻¹ • μ)
⊢ (μ Set.univ)⁻¹ ≠ 0
Module: Mathlib.Probability.Distributions.Fernique

Goal state before:
pp: case neg.h₂
E : Type u_1
inst✝⁵ : SeminormedAddCommGroup E
inst✝⁴ : NormedSpace ℝ E
inst✝³ : SecondCountableTopology E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsFiniteMeasure μ
h_rot✝ : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
hμ_zero : ¬μ = 0
μ' : Measure E := μ[|Set.univ]
hμ'_eq : μ' = (μ Set.univ)⁻¹ • μ
hμ' : IsProbabilityMeasure μ'
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ'.prod μ') = μ'.prod μ'
C : ℝ
hC_pos : 0 < C
hC : Integrable (fun x ↦ rexp (C * ‖x‖ ^ 2)) ((μ Set.univ)⁻¹ • μ)
⊢ (μ Set.univ)⁻¹ ≠ ∞

Tactic:
·

Goal state after:
case neg.h₂
E : Type u_1
inst✝⁵ : SeminormedAddCommGroup E
inst✝⁴ : NormedSpace ℝ E
inst✝³ : SecondCountableTopology E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsFiniteMeasure μ
h_rot✝ : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
hμ_zero : ¬μ = 0
μ' : Measure E := μ[|Set.univ]
hμ'_eq : μ' = (μ Set.univ)⁻¹ • μ
hμ' : IsProbabilityMeasure μ'
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ'.prod μ') = μ'.prod μ'
C : ℝ
hC_pos : 0 < C
hC : Integrable (fun x ↦ rexp (C * ‖x‖ ^ 2)) ((μ Set.univ)⁻¹ • μ)
⊢ (μ Set.univ)⁻¹ ≠ ∞
Module: Mathlib.Probability.BrownianMotion.Basic

Goal state before:
pp: case e'_2.e'_3
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
x✝ : Ω
⊢ B s x✝ = Function.eval ⟨s, ⋯⟩ ({s, t}.restrict fun x ↦ B x x✝)

pp: case e'_2.e'_4
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
x✝ : Ω
⊢ B t x✝ = {s, t}.restrict (fun x ↦ B x x✝) ⟨t, ⋯⟩

pp: case e'_3
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
⊢ ↑(min s t) = cov[Function.eval ⟨s, ⋯⟩, fun x ↦ x ⟨t, ⋯⟩; projectiveFamily {s, t}]

pp: case convert_1
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
⊢ AEMeasurable (Function.eval ⟨s, ⋯⟩) (projectiveFamily {s, t})

pp: case convert_2
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
⊢ AEMeasurable (fun x ↦ x ⟨t, ⋯⟩) (projectiveFamily {s, t})

Tactic:
·

Goal state after:
case e'_2.e'_3
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
x✝ : Ω
⊢ B s x✝ = Function.eval ⟨s, ⋯⟩ ({s, t}.restrict fun x ↦ B x x✝)
Module: Mathlib.Probability.BrownianMotion.Basic

Goal state before:
pp: case e'_2.e'_4
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
x✝ : Ω
⊢ B t x✝ = {s, t}.restrict (fun x ↦ B x x✝) ⟨t, ⋯⟩

pp: case e'_3
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
⊢ ↑(min s t) = cov[Function.eval ⟨s, ⋯⟩, fun x ↦ x ⟨t, ⋯⟩; projectiveFamily {s, t}]

pp: case convert_1
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
⊢ AEMeasurable (Function.eval ⟨s, ⋯⟩) (projectiveFamily {s, t})

pp: case convert_2
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
⊢ AEMeasurable (fun x ↦ x ⟨t, ⋯⟩) (projectiveFamily {s, t})

Tactic:
·

Goal state after:
case e'_2.e'_4
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
x✝ : Ω
⊢ B t x✝ = {s, t}.restrict (fun x ↦ B x x✝) ⟨t, ⋯⟩
Module: Mathlib.Probability.BrownianMotion.Basic

Goal state before:
pp: case e'_3
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
⊢ ↑(min s t) = cov[Function.eval ⟨s, ⋯⟩, fun x ↦ x ⟨t, ⋯⟩; projectiveFamily {s, t}]

pp: case convert_1
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
⊢ AEMeasurable (Function.eval ⟨s, ⋯⟩) (projectiveFamily {s, t})

pp: case convert_2
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
⊢ AEMeasurable (fun x ↦ x ⟨t, ⋯⟩) (projectiveFamily {s, t})

Tactic:
·

Goal state after:
case e'_3
Ω : Type u_1
mΩ : MeasurableSpace Ω
B : ℝ≥0 → Ω → ℝ
P : Measure Ω
hB : IsPreBrownianReal B P
s t : ℝ≥0
⊢ ↑(min s t) = cov[Function.eval ⟨s, ⋯⟩, fun x ↦ x ⟨t, ⋯⟩; projectiveFamily {s, t}]
Module: Mathlib.Probability.BrownianMotion.Basic

Goal state before:
pp: case hm
Ω : Type u_1
mΩ : MeasurableSpace Ω
X : ℝ≥0 → Ω → ℝ
P : Measure Ω
h1 : IsGaussianProcess X P
h2 : ∀ (t : ℝ≥0), ∫ (x : Ω), X t x ∂P = 0
h3 : ∀ (s t : ℝ≥0), s ≤ t → cov[X s, X t; P] = ↑s
I : Finset ℝ≥0
this : IsGaussian (Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P)
⊢ ∫ (x : WithLp 2 (↥I → ℝ)),
      id
        x ∂Measure.map (⇑(PiLp.continuousLinearEquiv 2 ℝ fun a ↦ ℝ).symm)
        (Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P) =
    ∫ (x : WithLp 2 (↥I → ℝ)), id x ∂Measure.map (⇑(PiLp.continuousLinearEquiv 2 ℝ fun a ↦ ℝ).symm) (projectiveFamily I)

pp: case hv
Ω : Type u_1
mΩ : MeasurableSpace Ω
X : ℝ≥0 → Ω → ℝ
P : Measure Ω
h1 : IsGaussianProcess X P
h2 : ∀ (t : ℝ≥0), ∫ (x : Ω), X t x ∂P = 0
h3 : ∀ (s t : ℝ≥0), s ≤ t → cov[X s, X t; P] = ↑s
I : Finset ℝ≥0
this : IsGaussian (Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P)
⊢ covarianceBilin
      (Measure.map (⇑(PiLp.continuousLinearEquiv 2 ℝ fun a ↦ ℝ).symm)
        (Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P)) =
    covarianceBilin (Measure.map (⇑(PiLp.continuousLinearEquiv 2 ℝ fun a ↦ ℝ).symm) (projectiveFamily I))

Tactic:
·

Goal state after:
case hm
Ω : Type u_1
mΩ : MeasurableSpace Ω
X : ℝ≥0 → Ω → ℝ
P : Measure Ω
h1 : IsGaussianProcess X P
h2 : ∀ (t : ℝ≥0), ∫ (x : Ω), X t x ∂P = 0
h3 : ∀ (s t : ℝ≥0), s ≤ t → cov[X s, X t; P] = ↑s
I : Finset ℝ≥0
this : IsGaussian (Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P)
⊢ ∫ (x : WithLp 2 (↥I → ℝ)),
      id
        x ∂Measure.map (⇑(PiLp.continuousLinearEquiv 2 ℝ fun a ↦ ℝ).symm)
        (Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P) =
    ∫ (x : WithLp 2 (↥I → ℝ)), id x ∂Measure.map (⇑(PiLp.continuousLinearEquiv 2 ℝ fun a ↦ ℝ).symm) (projectiveFamily I)
Module: Mathlib.Probability.Kernel.Disintegration.MeasurableStieltjes

Goal state before:
pp: case refine_1
α : Type u_1
f : α → ℚ → ℝ
inst✝ : MeasurableSpace α
hf : Measurable f
h1 : MeasurableSet {a | Monotone (f a)}
h2 : MeasurableSet {a | Tendsto (f a) atTop (𝓝 1)}
h3 : MeasurableSet {a | Tendsto (f a) atBot (𝓝 0)}
h4 : MeasurableSet {a | ∀ (t : ℚ), ⨅ r, f a ↑r = f a t}
a : α
h : IsRatStieltjesPoint f a
⊢ ((Monotone (f a) ∧ Tendsto (f a) atTop (𝓝 1)) ∧ Tendsto (f a) atBot (𝓝 0)) ∧ ∀ (t : ℚ), ⨅ r, f a ↑r = f a t

pp: case refine_2
α : Type u_1
f : α → ℚ → ℝ
inst✝ : MeasurableSpace α
hf : Measurable f
h1 : MeasurableSet {a | Monotone (f a)}
h2 : MeasurableSet {a | Tendsto (f a) atTop (𝓝 1)}
h3 : MeasurableSet {a | Tendsto (f a) atBot (𝓝 0)}
h4 : MeasurableSet {a | ∀ (t : ℚ), ⨅ r, f a ↑r = f a t}
a : α
h : ((Monotone (f a) ∧ Tendsto (f a) atTop (𝓝 1)) ∧ Tendsto (f a) atBot (𝓝 0)) ∧ ∀ (t : ℚ), ⨅ r, f a ↑r = f a t
⊢ IsRatStieltjesPoint f a

Tactic:
·

Goal state after:
case refine_1
α : Type u_1
f : α → ℚ → ℝ
inst✝ : MeasurableSpace α
hf : Measurable f
h1 : MeasurableSet {a | Monotone (f a)}
h2 : MeasurableSet {a | Tendsto (f a) atTop (𝓝 1)}
h3 : MeasurableSet {a | Tendsto (f a) atBot (𝓝 0)}
h4 : MeasurableSet {a | ∀ (t : ℚ), ⨅ r, f a ↑r = f a t}
a : α
h : IsRatStieltjesPoint f a
⊢ ((Monotone (f a) ∧ Tendsto (f a) atTop (𝓝 1)) ∧ Tendsto (f a) atBot (𝓝 0)) ∧ ∀ (t : ℚ), ⨅ r, f a ↑r = f a t
Module: Mathlib.Probability.Kernel.Disintegration.MeasurableStieltjes

Goal state before:
pp: case refine_2
α : Type u_1
f : α → ℚ → ℝ
inst✝ : MeasurableSpace α
hf : Measurable f
h1 : MeasurableSet {a | Monotone (f a)}
h2 : MeasurableSet {a | Tendsto (f a) atTop (𝓝 1)}
h3 : MeasurableSet {a | Tendsto (f a) atBot (𝓝 0)}
h4 : MeasurableSet {a | ∀ (t : ℚ), ⨅ r, f a ↑r = f a t}
a : α
h : ((Monotone (f a) ∧ Tendsto (f a) atTop (𝓝 1)) ∧ Tendsto (f a) atBot (𝓝 0)) ∧ ∀ (t : ℚ), ⨅ r, f a ↑r = f a t
⊢ IsRatStieltjesPoint f a

Tactic:
·

Goal state after:
case refine_2
α : Type u_1
f : α → ℚ → ℝ
inst✝ : MeasurableSpace α
hf : Measurable f
h1 : MeasurableSet {a | Monotone (f a)}
h2 : MeasurableSet {a | Tendsto (f a) atTop (𝓝 1)}
h3 : MeasurableSet {a | Tendsto (f a) atBot (𝓝 0)}
h4 : MeasurableSet {a | ∀ (t : ℚ), ⨅ r, f a ↑r = f a t}
a : α
h : ((Monotone (f a) ∧ Tendsto (f a) atTop (𝓝 1)) ∧ Tendsto (f a) atBot (𝓝 0)) ∧ ∀ (t : ℚ), ⨅ r, f a ↑r = f a t
⊢ IsRatStieltjesPoint f a
Module: Mathlib.Probability.BrownianMotion.Basic

Goal state before:
pp: case hm
Ω : Type u_1
mΩ : MeasurableSpace Ω
X : ℝ≥0 → Ω → ℝ
P : Measure Ω
h1 : IsGaussianProcess X P
h2 : ∀ (t : ℝ≥0), ∫ (x : Ω), X t x ∂P = 0
h3 : ∀ (s t : ℝ≥0), s ≤ t → cov[X s, X t; P] = ↑s
I : Finset ℝ≥0
this : IsGaussian (Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P)
i : ↥I
⊢ ∫ (x : Ω), I.restrict (fun x_1 ↦ X x_1 x) i ∂P = 0 i

pp: case hm.hf
Ω : Type u_1
mΩ : MeasurableSpace Ω
X : ℝ≥0 → Ω → ℝ
P : Measure Ω
h1 : IsGaussianProcess X P
h2 : ∀ (t : ℝ≥0), ∫ (x : Ω), X t x ∂P = 0
h3 : ∀ (s t : ℝ≥0), s ≤ t → cov[X s, X t; P] = ↑s
I : Finset ℝ≥0
this : IsGaussian (Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P)
i : ↥I
⊢ ∀ (i : ↥I), Integrable (fun x ↦ I.restrict (fun x_1 ↦ X x_1 x) i) P

Tactic:
·

Goal state after:
case hm
Ω : Type u_1
mΩ : MeasurableSpace Ω
X : ℝ≥0 → Ω → ℝ
P : Measure Ω
h1 : IsGaussianProcess X P
h2 : ∀ (t : ℝ≥0), ∫ (x : Ω), X t x ∂P = 0
h3 : ∀ (s t : ℝ≥0), s ≤ t → cov[X s, X t; P] = ↑s
I : Finset ℝ≥0
this : IsGaussian (Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P)
i : ↥I
⊢ ∫ (x : Ω), I.restrict (fun x_1 ↦ X x_1 x) i ∂P = 0 i
Module: Mathlib.Probability.BrownianMotion.Basic

Goal state before:
pp: case hm.hf
Ω : Type u_1
mΩ : MeasurableSpace Ω
X : ℝ≥0 → Ω → ℝ
P : Measure Ω
h1 : IsGaussianProcess X P
h2 : ∀ (t : ℝ≥0), ∫ (x : Ω), X t x ∂P = 0
h3 : ∀ (s t : ℝ≥0), s ≤ t → cov[X s, X t; P] = ↑s
I : Finset ℝ≥0
this : IsGaussian (Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P)
i : ↥I
⊢ ∀ (i : ↥I), Integrable (fun x ↦ I.restrict (fun x_1 ↦ X x_1 x) i) P

Tactic:
·

Goal state after:
case hm.hf
Ω : Type u_1
mΩ : MeasurableSpace Ω
X : ℝ≥0 → Ω → ℝ
P : Measure Ω
h1 : IsGaussianProcess X P
h2 : ∀ (t : ℝ≥0), ∫ (x : Ω), X t x ∂P = 0
h3 : ∀ (s t : ℝ≥0), s ≤ t → cov[X s, X t; P] = ↑s
I : Finset ℝ≥0
this : IsGaussian (Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P)
i : ↥I
⊢ ∀ (i : ↥I), Integrable (fun x ↦ I.restrict (fun x_1 ↦ X x_1 x) i) P