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

Goal state before:
pp: case zero
Ω : Type u_1
a b : ℝ
f : ℕ → Ω → ℝ
ω : Ω
hab : a < b
hω : ∃ k, ∀ (N : ℕ), upcrossingsBefore a b f N ω < k
h₁ : ∀ (a_1 : ℕ), ∃ b, a_1 ≤ b ∧ f b ω < a
h₂ : ∀ (a : ℕ), ∃ b_1, a ≤ b_1 ∧ b < f b_1 ω
⊢ ∃ N, 0 ≤ upcrossingsBefore a b f N ω

Tactic:
| zero =>

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

Goal state before:
pp: Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝³ : ConditionallyCompleteLinearOrder ι
inst✝² : TopologicalSpace ι
inst✝¹ : OrderTopology ι
inst✝ : FirstCountableTopology ι
f : Filtration ι m
τ : Ω → WithTop ι
hf : f.IsRightContinuous
hτ1 : ∀ (i : ι), MeasurableSet {ω | τ ω < ↑i}
hτ2 : ∀ (i : ι), 𝓝[>] i = ⊥ → MeasurableSet {ω | τ ω = ↑i}
t : ι
ht : ¬𝓝[>] t = ⊥
this : (𝓝[>] t).NeBot
s : ℕ → ι
hs_gt : ∀ (n : ℕ), t < s n
hs_tendsto : Tendsto s atTop (𝓝 t)
h_exists_lt : ∀ (u : ι), t < u → ∃ i, s i < u
h_exists_lt' : ∀ (u : WithTop ι), ↑t < u → ∃ i, ↑(s i) < u
h_eq_iInter : {ω | τ ω ≤ ↑t} = ⋂ m, {ω | τ ω < ↑(s m)}
⊢ ↑f t = ⨅ m_1, ↑f (s m_1)

Tactic:
← hf.eq,

Goal state after:
Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝³ : ConditionallyCompleteLinearOrder ι
inst✝² : TopologicalSpace ι
inst✝¹ : OrderTopology ι
inst✝ : FirstCountableTopology ι
f : Filtration ι m
τ : Ω → WithTop ι
hf : f.IsRightContinuous
hτ1 : ∀ (i : ι), MeasurableSet {ω | τ ω < ↑i}
hτ2 : ∀ (i : ι), 𝓝[>] i = ⊥ → MeasurableSet {ω | τ ω = ↑i}
t : ι
ht : ¬𝓝[>] t = ⊥
this : (𝓝[>] t).NeBot
s : ℕ → ι
hs_gt : ∀ (n : ℕ), t < s n
hs_tendsto : Tendsto s atTop (𝓝 t)
h_exists_lt : ∀ (u : ι), t < u → ∃ i, s i < u
h_exists_lt' : ∀ (u : WithTop ι), ↑t < u → ∃ i, ↑(s i) < u
h_eq_iInter : {ω | τ ω ≤ ↑t} = ⋂ m, {ω | τ ω < ↑(s m)}
⊢ ↑f₊ t = ⨅ m_1, ↑f (s m_1)
Module: Mathlib.Probability.Process.HittingTime

Goal state before:
pp: Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝¹ : ConditionallyCompleteLinearOrder ι
inst✝ : WellFoundedLT ι
u : ι → Ω → β
s : Set β
n m : ι
ω : Ω
h : ∃ j ∈ Set.Icc n m, u j ω ∈ s
⊢ stoppedValue u (fun ω ↦ ↑(hittingBtwn u s n m ω)) ω ∈ s

Tactic:
simp only [stoppedValue, hittingBtwn, if_pos h]
  obtain ⟨j, hj₁, hj₂⟩ := h
  have : sInf (Set.Icc n m ∩ {i | u i ω ∈ s}) ∈ Set.Icc n m ∩ {i | u i ω ∈ s} :=
    csInf_mem (Set.nonempty_of_mem ⟨hj₁, hj₂⟩)
  exact this.2

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

Goal state before:
pp: case neg
Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝³ : ConditionallyCompleteLinearOrder ι
inst✝² : TopologicalSpace ι
inst✝¹ : OrderTopology ι
inst✝ : FirstCountableTopology ι
f : Filtration ι m
τ : Ω → WithTop ι
hf : f.IsRightContinuous
hτ1 : ∀ (i : ι), MeasurableSet {ω | τ ω < ↑i}
hτ2 : ∀ (i : ι), 𝓝[>] i = ⊥ → MeasurableSet {ω | τ ω = ↑i}
t : ι
ht : ¬𝓝[>] t = ⊥
this : (𝓝[>] t).NeBot
s : ℕ → ι
hs_gt : ∀ (n : ℕ), t < s n
hs_tendsto : Tendsto s atTop (𝓝 t)
h_exists_lt : ∀ (u : ι), t < u → ∃ i, s i < u
h_exists_lt' : ∀ (u : WithTop ι), ↑t < u → ∃ i, ↑(s i) < u
h_eq_iInter : {ω | τ ω ≤ ↑t} = ⋂ m, {ω | τ ω < ↑(s m)}
h𝓕_eq_iInf : ↑f t = ⨅ m_1, ↑f (s m_1)
k : ℕ
h_eq_k : ⋂ m, {ω | τ ω < ↑(s m)} = ⋂ m, ⋂ (_ : s m ≤ s k), {ω | τ ω < ↑(s m)}
⊢ MeasurableSet (⋂ m, {ω | τ ω < ↑(s m)})

Tactic:
h_eq_k

Goal state after:
case neg
Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝³ : ConditionallyCompleteLinearOrder ι
inst✝² : TopologicalSpace ι
inst✝¹ : OrderTopology ι
inst✝ : FirstCountableTopology ι
f : Filtration ι m
τ : Ω → WithTop ι
hf : f.IsRightContinuous
hτ1 : ∀ (i : ι), MeasurableSet {ω | τ ω < ↑i}
hτ2 : ∀ (i : ι), 𝓝[>] i = ⊥ → MeasurableSet {ω | τ ω = ↑i}
t : ι
ht : ¬𝓝[>] t = ⊥
this : (𝓝[>] t).NeBot
s : ℕ → ι
hs_gt : ∀ (n : ℕ), t < s n
hs_tendsto : Tendsto s atTop (𝓝 t)
h_exists_lt : ∀ (u : ι), t < u → ∃ i, s i < u
h_exists_lt' : ∀ (u : WithTop ι), ↑t < u → ∃ i, ↑(s i) < u
h_eq_iInter : {ω | τ ω ≤ ↑t} = ⋂ m, {ω | τ ω < ↑(s m)}
h𝓕_eq_iInf : ↑f t = ⨅ m_1, ↑f (s m_1)
k : ℕ
h_eq_k : ⋂ m, {ω | τ ω < ↑(s m)} = ⋂ m, ⋂ (_ : s m ≤ s k), {ω | τ ω < ↑(s m)}
⊢ MeasurableSet (⋂ m, ⋂ (_ : s m ≤ s k), {ω | τ ω < ↑(s m)})
Module: Mathlib.Probability.Martingale.Upcrossing

Goal state before:
pp: case zero
Ω : Type u_1
a b : ℝ
f : ℕ → Ω → ℝ
N n : ℕ
hab : a < b
hab' : 0 < b - a
hf : ∀ (ω : Ω) (i : ℕ), b - a ≤ (f i ω - a)⁺ ↔ b ≤ f i ω
hf' : ∀ (ω : Ω) (i : ℕ), (f i ω - a)⁺ ≤ 0 ↔ f i ω ≤ a
⊢ upperCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N 0 = upperCrossingTime a b f N 0 ∧
    lowerCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N 0 = lowerCrossingTime a b f N 0

Tactic:
| zero =>

Goal state after:
Module: Mathlib.Probability.Martingale.Convergence

Goal state before:
pp: case compl
Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
ℱ : Filtration ℕ m0
inst✝ : IsFiniteMeasure μ
g : Ω → ℝ
hg : Integrable g μ
hgmeas : StronglyMeasurable g
hle : ⨆ n, ↑ℱ n ≤ m0
hunif : UniformIntegrable (fun n ↦ μ[g | ↑ℱ n]) 1 μ
R : ℝ≥0
hR : ∀ (i : ℕ), eLpNorm ((fun n ↦ μ[g | ↑ℱ n]) i) 1 μ ≤ ↑R
hlimint : Integrable (limitProcess (fun n ↦ μ[g | ↑ℱ n]) ℱ μ) μ
this :
  ∀ (n : ℕ) (s : Set Ω),
    MeasurableSet s → ∫ (x : Ω) in s, g x ∂μ = ∫ (x : Ω) in s, limitProcess (fun n x ↦ μ[g | ↑ℱ n] x) ℱ μ x ∂μ
s : Set Ω
hpi : IsPiSystem {s | ∃ n, MeasurableSet s}
t : Set Ω
htmeas : MeasurableSet t
ht : μ t < ∞ → ∫ (x : Ω) in t, g x ∂μ = ∫ (x : Ω) in t, limitProcess (fun n x ↦ μ[g | ↑ℱ n] x) ℱ μ x ∂μ
x✝ : μ tᶜ < ∞
hgeq : ∫ (x : Ω) in tᶜ, g x ∂μ.trim hle = ∫ (x : Ω), g x ∂μ.trim hle - ∫ (x : Ω) in t, g x ∂μ.trim hle
hheq :
  ∫ (x : Ω) in tᶜ, limitProcess (fun n ↦ μ[g | ↑ℱ n]) ℱ μ x ∂μ.trim hle =
    ∫ (x : Ω), limitProcess (fun n ↦ μ[g | ↑ℱ n]) ℱ μ x ∂μ.trim hle -
      ∫ (x : Ω) in t, limitProcess (fun n ↦ μ[g | ↑ℱ n]) ℱ μ x ∂μ.trim hle
⊢ ∫ (x : Ω) in tᶜ, g x ∂μ.trim hle = ∫ (x : Ω) in tᶜ, limitProcess (fun n x ↦ μ[g | ↑ℱ n] x) ℱ μ x ∂μ.trim hle

Tactic:
hgeq,

Goal state after:
case compl
Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
ℱ : Filtration ℕ m0
inst✝ : IsFiniteMeasure μ
g : Ω → ℝ
hg : Integrable g μ
hgmeas : StronglyMeasurable g
hle : ⨆ n, ↑ℱ n ≤ m0
hunif : UniformIntegrable (fun n ↦ μ[g | ↑ℱ n]) 1 μ
R : ℝ≥0
hR : ∀ (i : ℕ), eLpNorm ((fun n ↦ μ[g | ↑ℱ n]) i) 1 μ ≤ ↑R
hlimint : Integrable (limitProcess (fun n ↦ μ[g | ↑ℱ n]) ℱ μ) μ
this :
  ∀ (n : ℕ) (s : Set Ω),
    MeasurableSet s → ∫ (x : Ω) in s, g x ∂μ = ∫ (x : Ω) in s, limitProcess (fun n x ↦ μ[g | ↑ℱ n] x) ℱ μ x ∂μ
s : Set Ω
hpi : IsPiSystem {s | ∃ n, MeasurableSet s}
t : Set Ω
htmeas : MeasurableSet t
ht : μ t < ∞ → ∫ (x : Ω) in t, g x ∂μ = ∫ (x : Ω) in t, limitProcess (fun n x ↦ μ[g | ↑ℱ n] x) ℱ μ x ∂μ
x✝ : μ tᶜ < ∞
hgeq : ∫ (x : Ω) in tᶜ, g x ∂μ.trim hle = ∫ (x : Ω), g x ∂μ.trim hle - ∫ (x : Ω) in t, g x ∂μ.trim hle
hheq :
  ∫ (x : Ω) in tᶜ, limitProcess (fun n ↦ μ[g | ↑ℱ n]) ℱ μ x ∂μ.trim hle =
    ∫ (x : Ω), limitProcess (fun n ↦ μ[g | ↑ℱ n]) ℱ μ x ∂μ.trim hle -
      ∫ (x : Ω) in t, limitProcess (fun n ↦ μ[g | ↑ℱ n]) ℱ μ x ∂μ.trim hle
⊢ ∫ (x : Ω), g x ∂μ.trim hle - ∫ (x : Ω) in t, g x ∂μ.trim hle =
    ∫ (x : Ω) in tᶜ, limitProcess (fun n x ↦ μ[g | ↑ℱ n] x) ℱ μ x ∂μ.trim hle
Module: Mathlib.Probability.Martingale.OptionalStopping

Goal state before:
pp: Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
𝒢 : Filtration ℕ m0
f : ℕ → Ω → ℝ
inst✝ : IsFiniteMeasure μ
hsub : Submartingale f 𝒢 μ
hnonneg : 0 ≤ f
ε : ℝ≥0
n : ℕ
⊢ ENNReal.ofReal
        (∫ (ω : Ω) in {ω | ↑ε ≤ (range (n + 1)).sup' ⋯ fun k ↦ f k ω},
          stoppedValue f (fun ω ↦ ↑(hittingBtwn f {y | ↑ε ≤ y} 0 n ω)) ω ∂μ) +
      ENNReal.ofReal
        (∫ (ω : Ω) in {ω | ((range (n + 1)).sup' ⋯ fun k ↦ f k ω) < ↑ε},
          stoppedValue f (fun ω ↦ ↑(hittingBtwn f {y | ↑ε ≤ y} 0 n ω)) ω ∂μ) =
    ENNReal.ofReal (∫ (ω : Ω), stoppedValue f (fun ω ↦ ↑(hittingBtwn f {y | ↑ε ≤ y} 0 n ω)) ω ∂μ)

Tactic:
by
      rw [← ENNReal.ofReal_add, ← setIntegral_union]
      · rw [← setIntegral_univ (μ := μ)]
        convert! rfl
        ext ω
        change _ ↔ (ε : ℝ) ≤ _ ∨ _ < (ε : ℝ)
        simp only [le_or_gt, Set.mem_univ]
      · grind
      · exact measurableSet_lt (measurable_range_sup'' fun n _ =>
          (hsub.stronglyMeasurable n).measurable.le (𝒢.le n)) measurable_const
      · exact Integrable.integrableOn (hsub.integrable_stoppedValue
          (hsub.stronglyAdapted.adapted.isStoppingTime_hittingBtwn measurableSet_Ici)
          (fun ω ↦ mod_cast hittingBtwn_le ω))
      · exact Integrable.integrableOn (hsub.integrable_stoppedValue
          (hsub.stronglyAdapted.adapted.isStoppingTime_hittingBtwn measurableSet_Ici)
          (fun ω ↦ mod_cast hittingBtwn_le ω))
      exacts [integral_nonneg fun x => hnonneg _ _, integral_nonneg fun x => hnonneg _ _]

Goal state after:
Module: Mathlib.Probability.BorelCantelli

Goal state before:
pp: Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
ι : Type u_2
β : Type u_3
inst✝² : LinearOrder ι
mβ : MeasurableSpace β
inst✝¹ : NormedAddCommGroup β
inst✝ : BorelSpace β
f : ι → Ω → β
i j : ι
hf : ∀ (i : ι), StronglyMeasurable (f i)
hfi : iIndepFun f μ
hij : i < j
this : Indep (⨆ k ∈ {j}, MeasurableSpace.comap (f k) mβ) (⨆ k ∈ {k | k ≤ i}, MeasurableSpace.comap (f k) mβ) μ
⊢ Indep (MeasurableSpace.comap (f j) mβ) (↑(Filtration.natural f hf) i) μ

Tactic:
by rwa [iSup_singleton] at this

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

Goal state before:

Tactic:
t₁
    _ = a * t₁ + b * t₁ := by rw [← add_mul, hab, one_mul]
    _ ≤ a * t₁ + b * t₂ := by gcongr

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

Goal state before:
pp: case zero
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t : ℝ
ht : t ∈ interior (integrableExpSet X μ)
⊢ HasDerivAt (iteratedDeriv 0 (mgf X μ)) (∫ (x : Ω), (fun ω ↦ X ω ^ (0 + 1) * rexp (t * X ω)) x ∂μ) t

Tactic:
| zero =>

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

Goal state before:
pp: case zero
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t : ℝ
ht : t ∈ interior (integrableExpSet X μ)
⊢ iteratedDeriv 0 (mgf X μ) t = ∫ (x : Ω), (fun ω ↦ X ω ^ 0 * rexp (t * X ω)) x ∂μ

Tactic:
| zero =>

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

Goal state before:
pp: case e'_10.e'_8
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
v : ℝ
hv : v ∈ interior (integrableExpSet X μ)
e_5✝ : normedAddCommGroup = NonUnitalNormedRing.toNormedAddCommGroup
e_6✝ : RCLike.toInnerProductSpaceReal.toNormedSpace ≍ NormedField.toNormedSpace
x✝ : ℕ
⊢ ∫ (x : Ω), (fun ω ↦ X ω ^ x✝ * rexp (v * X ω)) x ∂μ = iteratedDeriv x✝ (mgf X μ) v

Tactic:
iteratedDeriv_mgf hv

Goal state after:
case e'_10.e'_8
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
v : ℝ
hv : v ∈ interior (integrableExpSet X μ)
e_5✝ : normedAddCommGroup = NonUnitalNormedRing.toNormedAddCommGroup
e_6✝ : RCLike.toInnerProductSpaceReal.toNormedSpace ≍ NormedField.toNormedSpace
x✝ : ℕ
⊢ ∫ (x : Ω), (fun ω ↦ X ω ^ x✝ * rexp (v * X ω)) x ∂μ = ∫ (x : Ω), (fun ω ↦ X ω ^ x✝ * rexp (v * X ω)) x ∂μ
Module: Mathlib.Probability.Moments.ComplexMGF

Goal state before:
pp: case zero
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
z : ℂ
hz : z.re ∈ interior (integrableExpSet X μ)
⊢ HasDerivAt (iteratedDeriv 0 (complexMGF X μ)) (∫ (x : Ω), (fun ω ↦ ↑(X ω) ^ (0 + 1) * cexp (z * ↑(X ω))) x ∂μ) z

Tactic:
| zero =>

Goal state after:
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: case neg
μ : ℝ
v : ℝ≥0
y : ℝ
hv : ¬v = 0
e : ℝ ≃ᵐ ℝ := (Homeomorph.addRight y).symm.toMeasurableEquiv
he' : ∀ (x : ℝ), HasDerivAt (⇑e) ((fun x ↦ 1) x) x
s' : Set ℝ
hs' : MeasurableSet s'
⊢ (Measure.map (⇑e.symm) (gaussianReal μ v)) s' = (gaussianReal (μ + y) v) s'

Tactic:
MeasurableEquiv.gaussianReal_map_symm_apply hv e he' hs'

Goal state after:
case neg
μ : ℝ
v : ℝ≥0
y : ℝ
hv : ¬v = 0
e : ℝ ≃ᵐ ℝ := (Homeomorph.addRight y).symm.toMeasurableEquiv
he' : ∀ (x : ℝ), HasDerivAt (⇑e) ((fun x ↦ 1) x) x
s' : Set ℝ
hs' : MeasurableSet s'
⊢ ENNReal.ofReal (∫ (x : ℝ) in s', |1| * gaussianPDFReal μ v (e x)) = (gaussianReal (μ + y) v) s'
Module: Mathlib.Probability.Moments.ComplexMGF

Goal state before:
pp: case zero
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
z : ℂ
hz : z.re ∈ interior (integrableExpSet X μ)
⊢ iteratedDeriv 0 (complexMGF X μ) z = ∫ (x : Ω), (fun ω ↦ ↑(X ω) ^ 0 * cexp (z * ↑(X ω))) x ∂μ

Tactic:
| zero =>

Goal state after:
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: μ : ℝ
v : ℝ≥0
y : ℝ
⊢ Measure.map (fun x ↦ y + x) (gaussianReal μ v) = gaussianReal (μ + y) v

Tactic:
simp_rw [add_comm y]
  exact gaussianReal_map_add_const y

Goal state after:
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: μ₁ μ₂ : ℝ
v₁ v₂ : ℝ≥0
h : gaussianReal μ₁ v₁ = gaussianReal μ₂ v₂
⊢ μ₁ = μ₂ ∧ v₁ = v₂

Tactic:
← integral_id_gaussianReal (μ := μ₁) (v := v₁),

Goal state after:
μ₁ μ₂ : ℝ
v₁ v₂ : ℝ≥0
h : gaussianReal μ₁ v₁ = gaussianReal μ₂ v₂
⊢ ∫ (x : ℝ), x ∂gaussianReal μ₁ v₁ = μ₂ ∧ v₁ = v₂
Module: Mathlib.Probability.Distributions.Gaussian.Basic

Goal state before:
pp: E : Type u_1
F : Type u_2
inst✝¹¹ : NormedAddCommGroup E
inst✝¹⁰ : NormedSpace ℝ E
inst✝⁹ : MeasurableSpace E
inst✝⁸ : BorelSpace E
inst✝⁷ : NormedAddCommGroup F
inst✝⁶ : NormedSpace ℝ F
inst✝⁵ : MeasurableSpace F
inst✝⁴ : BorelSpace F
μ✝ : Measure E
inst✝³ : IsGaussian μ✝
inst✝² : SecondCountableTopology E
μ ν : Measure E
inst✝¹ : IsGaussian μ
inst✝ : IsGaussian ν
L : StrongDual ℝ E
this : ∫ (x : E), L x ∂μ ∗ ν = ∫ (x : ℝ), x ∂Measure.map (⇑L) μ ∗ Measure.map (⇑L) ν
⊢ Measure.map (⇑L) (μ ∗ ν) = gaussianReal (∫ (x : E), L x ∂μ ∗ ν) Var[⇑L; μ ∗ ν].toNNReal

Tactic:
rw [Measure.map_conv_continuousLinearMap L, this, ← variance_id_map (by fun_prop),
      Measure.map_conv_continuousLinearMap L, IsGaussian.map_eq_gaussianReal L,
      IsGaussian.map_eq_gaussianReal L, gaussianReal_conv_gaussianReal]

Goal state after:
E : Type u_1
F : Type u_2
inst✝¹¹ : NormedAddCommGroup E
inst✝¹⁰ : NormedSpace ℝ E
inst✝⁹ : MeasurableSpace E
inst✝⁸ : BorelSpace E
inst✝⁷ : NormedAddCommGroup F
inst✝⁶ : NormedSpace ℝ F
inst✝⁵ : MeasurableSpace F
inst✝⁴ : BorelSpace F
μ✝ : Measure E
inst✝³ : IsGaussian μ✝
inst✝² : SecondCountableTopology E
μ ν : Measure E
inst✝¹ : IsGaussian μ
inst✝ : IsGaussian ν
L : StrongDual ℝ E
this : ∫ (x : E), L x ∂μ ∗ ν = ∫ (x : ℝ), x ∂Measure.map (⇑L) μ ∗ Measure.map (⇑L) ν
⊢ gaussianReal (∫ (x : E), L x ∂μ + ∫ (x : E), L x ∂ν) (Var[⇑L; μ].toNNReal + Var[⇑L; ν].toNNReal) =
    gaussianReal
      (∫ (x : ℝ), x ∂gaussianReal (∫ (x : E), L x ∂μ + ∫ (x : E), L x ∂ν) (Var[⇑L; μ].toNNReal + Var[⇑L; ν].toNNReal))
      Var[id; gaussianReal (∫ (x : E), L x ∂μ + ∫ (x : E), L x ∂ν) (Var[⇑L; μ].toNNReal + Var[⇑L; ν].toNNReal)].toNNReal
Module: Mathlib.Probability.Moments.CovarianceBilinDual

Goal state before:
pp: E : Type u_1
inst✝³ : NormedAddCommGroup E
mE : MeasurableSpace E
μ : Measure E
p : ℝ≥0∞
𝕜 : Type u_2
inst✝² : NontriviallyNormedField 𝕜
inst✝¹ : NormedSpace 𝕜 E
inst✝ : OpensMeasurableSpace E
L : StrongDual 𝕜 E
h_Lp : MemLp id p μ
hp : ¬p = 0
hp_top : ¬p = ∞
h0 : 0 < p.toReal
⊢ ∫⁻ (x : E), ‖L x‖ₑ ^ p.toReal ∂μ ≤ ∫⁻ (x : E), ‖L‖ₑ ^ p.toReal * ‖x‖ₑ ^ p.toReal ∂μ

Tactic:
by
    refine lintegral_mono fun x ↦ ?_
    rw [← ENNReal.mul_rpow_of_nonneg]
    swap; · positivity
    gcongr
    exact L.le_opENorm x

Goal state after:
Module: Mathlib.Probability.Distributions.Fernique

Goal state before:

Tactic:
(b - a) / √2 < (‖p.1 + p.2‖ / √2 - ‖p.1 - p.2‖ / √2) / √2 := by gcongr
        _ = (‖p.1 + p.2‖ - ‖p.1 - p.2‖) / 2 := by
          field_simp; rw [Real.sq_sqrt (by positivity)]; ring

Goal state after:
Module: Mathlib.Probability.Distributions.Fernique

Goal state before:
pp: case zero
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
a : ℝ
inst✝ : IsProbabilityMeasure μ
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
ha_gt : 2⁻¹ < μ {x | ‖x‖ ≤ a}
c : ℝ≥0∞ := μ {x | ‖x‖ ≤ a}
hc_gt : 2⁻¹ < c
hc_pos : 0 < c
hc_lt_top : c < ∞
⊢ μ {x | normThreshold a 0 < ‖x‖} ≤ μ {x | ‖x‖ ≤ a} * ((1 - μ {x | ‖x‖ ≤ a}) / μ {x | ‖x‖ ≤ a}) ^ 2 ^ 0

Tactic:
| zero =>

Goal state after:
Module: Mathlib.Probability.Distributions.Gaussian.Multivariate

Goal state before:
pp: E : Type u_2
inst✝⁴ : NormedAddCommGroup E
inst✝³ : InnerProductSpace ℝ E
inst✝² : FiniteDimensional ℝ E
inst✝¹ : MeasurableSpace E
inst✝ : BorelSpace E
L : StrongDual ℝ E
⊢ Var[⇑L ∘ fun x ↦ ∑ i, x i • (stdOrthonormalBasis ℝ E) i; Measure.pi fun x ↦ gaussianReal 0 1] = ‖L‖ ^ 2

Tactic:
have : L ∘ (fun x : Fin (Module.finrank ℝ E) → ℝ ↦ ∑ i, x i • stdOrthonormalBasis ℝ E i) =
      ∑ i, (fun x : Fin (Module.finrank ℝ E) → ℝ ↦ L (stdOrthonormalBasis ℝ E i) * x i) := by
    ext x; simp [mul_comm]

Goal state after:
E : Type u_2
inst✝⁴ : NormedAddCommGroup E
inst✝³ : InnerProductSpace ℝ E
inst✝² : FiniteDimensional ℝ E
inst✝¹ : MeasurableSpace E
inst✝ : BorelSpace E
L : StrongDual ℝ E
this : (⇑L ∘ fun x ↦ ∑ i, x i • (stdOrthonormalBasis ℝ E) i) = ∑ i, fun x ↦ L ((stdOrthonormalBasis ℝ E) i) * x i
⊢ Var[⇑L ∘ fun x ↦ ∑ i, x i • (stdOrthonormalBasis ℝ E) i; Measure.pi fun x ↦ gaussianReal 0 1] = ‖L‖ ^ 2
Module: Mathlib.Probability.Process.FiniteDimensionalLaws

Goal state before:
pp: T : Type u_1
Ω : Type u_2
𝓧 : T → Type u_3
mΩ : MeasurableSpace Ω
mα : (t : T) → MeasurableSpace (𝓧 t)
X Y : (t : T) → Ω → 𝓧 t
P : Measure Ω
h : ∀ (t : T), X t =ᵐ[P] Y t
I : Finset T
⊢ ∀ (i : ↥I), ∀ᵐ (a : Ω) ∂P, X (↑i) a = Y (↑i) a

Tactic:
exact fun i ↦ h i

Goal state after:
Module: Mathlib.Probability.Distributions.Fernique

Goal state before:
pp: 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
a : ℝ
inst✝ : IsProbabilityMeasure μ
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
c : ℝ≥0∞
hc_gt : 2⁻¹ < c
h_neg : a < 0
this : {x | ‖x‖ ≤ a} = ∅
hc : c = 0
⊢ False

Tactic:
simp [hc] at hc_gt

Goal state after:
Module: Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Basic

Goal state before:
pp: T : Type u_2
Ω : Type u_3
E : Type u_4
mΩ : MeasurableSpace Ω
P : Measure Ω
X : T → Ω → E
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : MeasurableSpace E
inst✝³ : BorelSpace E
inst✝² : NormedSpace ℝ E
inst✝¹ : SecondCountableTopology E
inst✝ : Add T
h : IsGaussianProcess X P
t₀ t : T
c : ℝ
x : ↥{t₀, t₀ + t} → E
⊢ (c • x) ⟨t₀ + t, ⋯⟩ - (c • x) ⟨t₀, ⋯⟩ = (RingHom.id ℝ) c • (x ⟨t₀ + t, ⋯⟩ - x ⟨t₀, ⋯⟩)

Tactic:
by simp; module

Goal state after:
Module: Mathlib.Probability.Distributions.Fernique

Goal state before:
pp: 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✝ : IsProbabilityMeasure μ
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
a : ℝ
ha_pos : 0 < a
ha_gt : 2⁻¹ < μ {x | ‖x‖ ≤ a}
ha_lt : μ {x | ‖x‖ ≤ a} < 1
⊢ ∃ C, 0 < C ∧ Integrable (fun x ↦ rexp (C * ‖x‖ ^ 2)) μ

Tactic:
let c := μ {x | ‖x‖ ≤ a}
  replace hc_lt : c < 1 := ha_lt
  have hc_lt_top : c < ∞ := measure_lt_top _ _
  have hc_one_sub_lt_top : 1 - c < ∞ := lt_top_of_lt (b := 2) (tsub_le_self.trans_lt (by simp))
  have h_one_sub_lt_self : 1 - c < c := by
    refine ENNReal.sub_lt_of_lt_add hc_lt.le ?_
    rw [← two_mul]
    rwa [inv_eq_one_div, ENNReal.div_lt_iff (by simp) (by simp), mul_comm] at ha_gt
  have h_pos : 0 < logRatio c * a⁻¹ ^ 2 := mul_pos (logRatio_pos ha_gt hc_lt) (by positivity)
  refine ⟨logRatio c * a⁻¹ ^ 2, h_pos, ⟨by fun_prop, ?_⟩⟩
  simp only [HasFiniteIntegral, ← ofReal_norm, Real.norm_eq_abs, Real.abs_exp]
  -- `⊢ ∫⁻ x, ENNReal.ofReal (rexp (logRatio c * a⁻¹ ^ 2 * ‖x‖ ^ 2)) ∂μ < ∞`
  refine (lintegral_exp_mul_sq_norm_le_of_map_rotation_eq_self h_rot le_rfl ha_gt).trans_lt ?_
  refine ENNReal.add_lt_top.mpr ⟨ENNReal.ofReal_lt_top, ?_⟩
  refine Summable.tsum_ofReal_lt_top <|
    Real.summable_exp_nat_mul_of_ge ?_ (fun i ↦ mod_cast (Nat.lt_pow_self (by simp)).le)
  refine mul_neg_of_neg_of_pos (by simp) (Real.log_pos ?_)
  change 1 < (c / (1 - c)).toReal
  simp only [ENNReal.toReal_div, one_lt_div_iff, ENNReal.toReal_pos_iff, tsub_pos_iff_lt, hc_lt,
    hc_one_sub_lt_top, and_self, true_and]
  rw [ENNReal.toReal_lt_toReal hc_one_sub_lt_top.ne hc_lt_top.ne]
  exact .inl h_one_sub_lt_self

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

Goal state before:
pp: 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 X' : (i : S) → (j : T i) → Ω → 𝓧 i j
h1 : iIndepFun (fun i ω j ↦ X i j ω) κ P
h2 : ∀ (i : S) (j : T i), ∀ᵐ (a : α) ∂P, X i j =ᵐ[κ a] X' i j
s : Finset S
f : S → Set Ω
g : (i : S) → Set ((fun i ↦ (j : T i) → 𝓧 i j) i)
mg : ∀ i ∈ s, MeasurableSet (g i)
hg : ∀ i ∈ s, (fun i ω j ↦ X' i j ω) i ⁻¹' g i = f i
h3 : ⋂ i ∈ s, f i = ⋂ i ∈ s, (fun i ω j ↦ X' i j ω) i ⁻¹' g i
a : α
⊢ ∏ i ∈ s, (κ a) (f i) = ∏ i ∈ s, (κ a) ((fun i ω j ↦ X' i j ω) i ⁻¹' g i)

Tactic:
refine Finset.prod_congr rfl fun i hi ↦ ?_

Goal state after:
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 X' : (i : S) → (j : T i) → Ω → 𝓧 i j
h1 : iIndepFun (fun i ω j ↦ X i j ω) κ P
h2 : ∀ (i : S) (j : T i), ∀ᵐ (a : α) ∂P, X i j =ᵐ[κ a] X' i j
s : Finset S
f : S → Set Ω
g : (i : S) → Set ((fun i ↦ (j : T i) → 𝓧 i j) i)
mg : ∀ i ∈ s, MeasurableSet (g i)
hg : ∀ i ∈ s, (fun i ω j ↦ X' i j ω) i ⁻¹' g i = f i
h3 : ⋂ i ∈ s, f i = ⋂ i ∈ s, (fun i ω j ↦ X' i j ω) i ⁻¹' g i
a : α
i : S
hi : i ∈ s
⊢ (κ a) (f i) = (κ a) ((fun i ω j ↦ X' i j ω) i ⁻¹' g i)
Module: Mathlib.Probability.Independence.Process.Basic

Goal state before:
pp: 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 X' : (i : S) → (j : T i) → Ω → 𝓧 i j
h1 : iIndepFun (fun i ω j ↦ X i j ω) κ P
h2 : ∀ (i : S) (j : T i), ∀ᵐ (a : α) ∂P, X i j =ᵐ[κ a] X' i j
s : Finset S
f✝ : S → Set Ω
g : (i : S) → Set ((fun i ↦ (j : T i) → 𝓧 i j) i)
mg : ∀ i ∈ s, MeasurableSet (g i)
hg : ∀ i ∈ s, (fun i ω j ↦ X' i j ω) i ⁻¹' g i = f✝ i
h3 : ⋂ i ∈ s, f✝ i = ⋂ i ∈ s, (fun i ω j ↦ X' i j ω) i ⁻¹' g i
h3' : ∀ (a : α), ∏ i ∈ s, (κ a) (f✝ i) = ∏ i ∈ s, (κ a) ((fun i ω j ↦ X' i j ω) i ⁻¹' g i)
I : (i : S) → Set (T i)
u : (i : S) → Set ((a : ↑(I i)) → 𝓧 i ↑a)
hI : ∀ i ∈ s, (I i).Countable
hu : ∀ i ∈ s, g i = (I i).domRestrict ⁻¹' u i
h4 :
  ∀ (f : (i : S) → (j : T i) → Ω → 𝓧 i j),
    ⋂ i ∈ s, (fun i ω j ↦ f i j ω) i ⁻¹' g i = ⋂ i ∈ s, (fun i ω j ↦ f i j ω) i ⁻¹' (I i).domRestrict ⁻¹' u i
a : α
f : (i : S) → (j : T i) → Ω → 𝓧 i j
⊢ ∏ i ∈ s, (κ a) ((fun i ω j ↦ f i j ω) i ⁻¹' g i) =
    ∏ i ∈ s, (κ a) ((fun i ω j ↦ f i j ω) i ⁻¹' (I i).domRestrict ⁻¹' u i)

Tactic:
refine Finset.prod_congr rfl fun i hi ↦ ?_

Goal state after:
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 X' : (i : S) → (j : T i) → Ω → 𝓧 i j
h1 : iIndepFun (fun i ω j ↦ X i j ω) κ P
h2 : ∀ (i : S) (j : T i), ∀ᵐ (a : α) ∂P, X i j =ᵐ[κ a] X' i j
s : Finset S
f✝ : S → Set Ω
g : (i : S) → Set ((fun i ↦ (j : T i) → 𝓧 i j) i)
mg : ∀ i ∈ s, MeasurableSet (g i)
hg : ∀ i ∈ s, (fun i ω j ↦ X' i j ω) i ⁻¹' g i = f✝ i
h3 : ⋂ i ∈ s, f✝ i = ⋂ i ∈ s, (fun i ω j ↦ X' i j ω) i ⁻¹' g i
h3' : ∀ (a : α), ∏ i ∈ s, (κ a) (f✝ i) = ∏ i ∈ s, (κ a) ((fun i ω j ↦ X' i j ω) i ⁻¹' g i)
I : (i : S) → Set (T i)
u : (i : S) → Set ((a : ↑(I i)) → 𝓧 i ↑a)
hI : ∀ i ∈ s, (I i).Countable
hu : ∀ i ∈ s, g i = (I i).domRestrict ⁻¹' u i
h4 :
  ∀ (f : (i : S) → (j : T i) → Ω → 𝓧 i j),
    ⋂ i ∈ s, (fun i ω j ↦ f i j ω) i ⁻¹' g i = ⋂ i ∈ s, (fun i ω j ↦ f i j ω) i ⁻¹' (I i).domRestrict ⁻¹' u i
a : α
f : (i : S) → (j : T i) → Ω → 𝓧 i j
i : S
hi : i ∈ s
⊢ (κ a) ((fun i ω j ↦ f i j ω) i ⁻¹' g i) = (κ a) ((fun i ω j ↦ f i j ω) i ⁻¹' (I i).domRestrict ⁻¹' u i)