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

Goal state before:
pp: case pos
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
k : ℕ
ih :
  upperCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N k = upperCrossingTime a b f N k ∧
    lowerCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N k = lowerCrossingTime a b f N k
this : upperCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N (k + 1) = upperCrossingTime a b f N (k + 1)
ω : Ω
h₁ : ¬∃ j ∈ Set.Icc (upperCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N (k + 1) ω) N, (f j ω - a)⁺ ∈ Set.Iic 0
h₂ : ∃ j ∈ Set.Icc (upperCrossingTime a b f N (k + 1) ω) N, f j ω ∈ Set.Iic a
⊢ N = sInf (Set.Icc (upperCrossingTime a b f N (k + 1) ω) N ∩ {i | f i ω ≤ a})

pp: case neg
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
k : ℕ
ih :
  upperCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N k = upperCrossingTime a b f N k ∧
    lowerCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N k = lowerCrossingTime a b f N k
this : upperCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N (k + 1) = upperCrossingTime a b f N (k + 1)
ω : Ω
h₁ : ¬∃ j ∈ Set.Icc (upperCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N (k + 1) ω) N, (f j ω - a)⁺ ∈ Set.Iic 0
h₂ : ¬∃ j ∈ Set.Icc (upperCrossingTime a b f N (k + 1) ω) N, f j ω ∈ Set.Iic a
⊢ N = N

Tactic:
· refine False.elim (h₁ ?_)
      simp_all only [Set.mem_Iic]

Goal state after:
case neg
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
k : ℕ
ih :
  upperCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N k = upperCrossingTime a b f N k ∧
    lowerCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N k = lowerCrossingTime a b f N k
this : upperCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N (k + 1) = upperCrossingTime a b f N (k + 1)
ω : Ω
h₁ : ¬∃ j ∈ Set.Icc (upperCrossingTime 0 (b - a) (fun n ω ↦ (f n ω - a)⁺) N (k + 1) ω) N, (f j ω - a)⁺ ∈ Set.Iic 0
h₂ : ¬∃ j ∈ Set.Icc (upperCrossingTime a b f N (k + 1) ω) N, f j ω ∈ Set.Iic a
⊢ N = N
Module: Mathlib.Probability.Process.Stopping

Goal state before:
pp: Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝³ : LinearOrder ι
f : Filtration ι m
τ π : Ω → WithTop ι
inst✝² : TopologicalSpace ι
inst✝¹ : SecondCountableTopology ι
inst✝ : OrderTopology ι
hτ : IsStoppingTime f τ
hπ : IsStoppingTime f π
⊢ MeasurableSet {ω | τ ω ≤ π ω}

Tactic:
exact measurableSet_le_stopping_time hτ hπ

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

Goal state before:
pp: case neg.refine_1
Ω : Type u_2
m0 : MeasurableSpace Ω
μ : Measure Ω
f : ℕ → Ω → ℝ
r : ℝ
R : ℝ≥0
hr : 0 ≤ r
hf0 : f 0 = 0
hbdd : ∀ᵐ (ω : Ω) ∂μ, ∀ (i : ℕ), |f (i + 1) ω - f i ω| ≤ ↑R
i : ℕ
ω : Ω
hbddω : ∀ (i : ℕ), |f (i + 1) ω - f i ω| ≤ ↑R
k p : ℕ
h_zero : ¬p = 0
hk : p = k + 1
⊢ k < p

Tactic:
lia

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

Goal state before:
pp: case coe
Ω : Type u_1
β : Type u_2
ι : Type u_3
m : MeasurableSpace Ω
inst✝⁸ : Nonempty ι
u : ι → Ω → β
τ : Ω → WithTop ι
inst✝⁷ : LinearOrder ι
inst✝⁶ : MeasurableSpace ι
inst✝⁵ : TopologicalSpace ι
inst✝⁴ : OrderTopology ι
inst✝³ : SecondCountableTopology ι
inst✝² : BorelSpace ι
inst✝¹ : TopologicalSpace β
f : Filtration ι m
inst✝ : PseudoMetrizableSpace ι
h : IsStronglyProgressive f u
hτ : IsStoppingTime f τ
h_meas : IsStronglyProgressive f fun i ω ↦ (min (↑i) (τ ω)).untopA
i : ι
ω : Ω
t : ι
⊢ (min ↑i ↑t).untopA ≤ i

Tactic:
rcases le_total i t with h_it | h_ti

Goal state after:
case coe.inl
Ω : Type u_1
β : Type u_2
ι : Type u_3
m : MeasurableSpace Ω
inst✝⁸ : Nonempty ι
u : ι → Ω → β
τ : Ω → WithTop ι
inst✝⁷ : LinearOrder ι
inst✝⁶ : MeasurableSpace ι
inst✝⁵ : TopologicalSpace ι
inst✝⁴ : OrderTopology ι
inst✝³ : SecondCountableTopology ι
inst✝² : BorelSpace ι
inst✝¹ : TopologicalSpace β
f : Filtration ι m
inst✝ : PseudoMetrizableSpace ι
h : IsStronglyProgressive f u
hτ : IsStoppingTime f τ
h_meas : IsStronglyProgressive f fun i ω ↦ (min (↑i) (τ ω)).untopA
i : ι
ω : Ω
t : ι
h_it : i ≤ t
⊢ (min ↑i ↑t).untopA ≤ i

case coe.inr
Ω : Type u_1
β : Type u_2
ι : Type u_3
m : MeasurableSpace Ω
inst✝⁸ : Nonempty ι
u : ι → Ω → β
τ : Ω → WithTop ι
inst✝⁷ : LinearOrder ι
inst✝⁶ : MeasurableSpace ι
inst✝⁵ : TopologicalSpace ι
inst✝⁴ : OrderTopology ι
inst✝³ : SecondCountableTopology ι
inst✝² : BorelSpace ι
inst✝¹ : TopologicalSpace β
f : Filtration ι m
inst✝ : PseudoMetrizableSpace ι
h : IsStronglyProgressive f u
hτ : IsStoppingTime f τ
h_meas : IsStronglyProgressive f fun i ω ↦ (min (↑i) (τ ω)).untopA
i : ι
ω : Ω
t : ι
h_ti : t ≤ i
⊢ (min ↑i ↑t).untopA ≤ i
Module: Mathlib.Probability.Moments.Basic

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t : ℝ
ν : Measure Ω
hμ : Integrable (fun ω ↦ rexp (t * X ω)) μ
hν : Integrable (fun ω ↦ rexp (t * X ω)) ν
⊢ mgf X (μ + ν) t = mgf X μ t + mgf X ν t

Tactic:
rw [mgf, integral_add_measure hμ hν, mgf, mgf]

Goal state after:
Module: Mathlib.Probability.BorelCantelli

Goal state before:
pp: case refine_2
Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
s : ℕ → Set Ω
hsm : ∀ (n : ℕ), MeasurableSet (s n)
hs : iIndepSet s μ
hs' : ∑' (n : ℕ), μ (s n) = ∞
this✝ : IsProbabilityMeasure μ
this :
  ∀ᵐ (ω : Ω) ∂μ, ∀ (n : ℕ), μ[(s (n + 1)).indicator 1 | ↑(filtrationOfSet hsm) n] ω = (fun x ↦ μ.real (s (n + 1))) ω
ω : Ω
hω : ∀ (n : ℕ), μ[(s (n + 1)).indicator 1 | ↑(filtrationOfSet hsm) n] ω = μ.real (s (n + 1))
htends : ∀ (x : NNReal), ∀ᶠ (a : ℕ) in atTop, ↑x < ∑ k ∈ Finset.range a, μ (s (k + 1))
B : ℝ
n : ℕ
hB : (∑ k ∈ Finset.range n, μ (s (k + 1))).toReal ≤ B
⊢ ¬↑B.toNNReal < ∑ k ∈ Finset.range n, μ (s (k + 1))

Tactic:
rwa [not_lt, ENNReal.ofNNReal_toNNReal, ENNReal.le_ofReal_iff_toReal_le]

Goal state after:
case refine_2.ha
Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
s : ℕ → Set Ω
hsm : ∀ (n : ℕ), MeasurableSet (s n)
hs : iIndepSet s μ
hs' : ∑' (n : ℕ), μ (s n) = ∞
this✝ : IsProbabilityMeasure μ
this :
  ∀ᵐ (ω : Ω) ∂μ, ∀ (n : ℕ), μ[(s (n + 1)).indicator 1 | ↑(filtrationOfSet hsm) n] ω = (fun x ↦ μ.real (s (n + 1))) ω
ω : Ω
hω : ∀ (n : ℕ), μ[(s (n + 1)).indicator 1 | ↑(filtrationOfSet hsm) n] ω = μ.real (s (n + 1))
htends : ∀ (x : NNReal), ∀ᶠ (a : ℕ) in atTop, ↑x < ∑ k ∈ Finset.range a, μ (s (k + 1))
B : ℝ
n : ℕ
hB : (∑ k ∈ Finset.range n, μ (s (k + 1))).toReal ≤ B
⊢ ∑ k ∈ Finset.range n, μ (s (k + 1)) ≠ ∞

case refine_2.hb
Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
s : ℕ → Set Ω
hsm : ∀ (n : ℕ), MeasurableSet (s n)
hs : iIndepSet s μ
hs' : ∑' (n : ℕ), μ (s n) = ∞
this✝ : IsProbabilityMeasure μ
this :
  ∀ᵐ (ω : Ω) ∂μ, ∀ (n : ℕ), μ[(s (n + 1)).indicator 1 | ↑(filtrationOfSet hsm) n] ω = (fun x ↦ μ.real (s (n + 1))) ω
ω : Ω
hω : ∀ (n : ℕ), μ[(s (n + 1)).indicator 1 | ↑(filtrationOfSet hsm) n] ω = μ.real (s (n + 1))
htends : ∀ (x : NNReal), ∀ᶠ (a : ℕ) in atTop, ↑x < ∑ k ∈ Finset.range a, μ (s (k + 1))
B : ℝ
n : ℕ
hB : (∑ k ∈ Finset.range n, μ (s (k + 1))).toReal ≤ B
⊢ 0 ≤ B
Module: Mathlib.Probability.Moments.Basic

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
μ : Measure Ω
t : ℝ
X Y : Ω → ℝ
h_int_X : AEStronglyMeasurable (fun ω ↦ rexp (t * X ω)) μ
h_int_Y : AEStronglyMeasurable (fun ω ↦ rexp (t * Y ω)) μ
⊢ AEStronglyMeasurable (fun ω ↦ rexp (t * (X + Y) ω)) μ

Tactic:
by
  simp_rw [Pi.add_apply, mul_add, exp_add]
  exact AEStronglyMeasurable.mul h_int_X h_int_Y

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

Goal state before:

Tactic:
‖∫ ω, cexp (z.re * X ω) * cexp (z.im * I * X ω) ∂μ‖
  _ ≤ ∫ ω, ‖cexp (z.re * X ω) * cexp (z.im * I * X ω)‖ ∂μ := norm_integral_le_integral_norm _
  _ = ∫ ω, rexp (z.re * X ω) ∂μ := by simp [Complex.norm_exp]

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

Goal state before:
pp: case refine_1
Ω : Type u_2
m0 : MeasurableSpace Ω
μ : Measure Ω
ℱ : Filtration ℕ m0
inst✝ : IsFiniteMeasure μ
s : ℕ → Set Ω
hs : ∀ (n : ℕ), MeasurableSet (s n)
ω : Ω
n : ℕ
⊢ process s n ω ≤ process s (n + 1) ω

Tactic:
rw [process, process, ← sub_nonneg, Finset.sum_apply, Finset.sum_apply,
      Finset.sum_range_succ_sub_sum]
    exact Set.indicator_nonneg (fun _ _ => zero_le_one) _

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

Goal state before:
pp: μ : ℝ
v : ℝ≥0
hv : ¬v = 0
g : ℝ → ℝ := fun x ↦ (√(2 * π * ↑v))⁻¹ * rexp (-x ^ 2 / (2 * ↑v))
this : g = fun x ↦ (√(2 * π * ↑v))⁻¹ * rexp (-↑(2 * v)⁻¹ * x ^ 2)
⊢ 0 < ↑(2 * v)⁻¹

Tactic:
simpa [pos_iff_ne_zero]

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

Goal state before:
pp: case pos
μ : ℝ
v : ℝ≥0
hv : v = 0
⊢ Measure.dirac μ = Measure.dirac μ + ℙ.withDensity 0

Tactic:
withDensity_zero,

Goal state after:
case pos
μ : ℝ
v : ℝ≥0
hv : v = 0
⊢ Measure.dirac μ = Measure.dirac μ + 0
Module: Mathlib.Probability.Moments.MGFAnalytic

Goal state before:
pp: case e_a
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
v : ℝ
h : v ∈ interior (integrableExpSet X μ)
hμ : ¬μ = 0
⊢ ∫ (ω : Ω), X ω ^ 2 * rexp (v * X ω) ∂μ - (2 * ∫ (ω : Ω), X ω * rexp (v * X ω) ∂μ) * deriv (cgf X μ) v +
      deriv (cgf X μ) v ^ 2 * mgf X μ v =
    ∫ (ω : Ω), (X ω ^ 2 - 2 * X ω * deriv (cgf X μ) v) * rexp (v * X ω) + deriv (cgf X μ) v ^ 2 * rexp (v * X ω) ∂μ

Tactic:
sub_mul

Goal state after:
case e_a
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
v : ℝ
h : v ∈ interior (integrableExpSet X μ)
hμ : ¬μ = 0
⊢ ∫ (ω : Ω), X ω ^ 2 * rexp (v * X ω) ∂μ - (2 * ∫ (ω : Ω), X ω * rexp (v * X ω) ∂μ) * deriv (cgf X μ) v +
      deriv (cgf X μ) v ^ 2 * mgf X μ v =
    ∫ (ω : Ω),
      X ω ^ 2 * rexp (v * X ω) - 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω) +
        deriv (cgf X μ) v ^ 2 * rexp (v * X ω) ∂μ
Module: Mathlib.Probability.Distributions.Gaussian.Basic

Goal state before:
pp: E : Type u_1
inst✝⁴ : NormedAddCommGroup E
inst✝³ : NormedSpace ℝ E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsGaussian μ
L : StrongDual ℝ E
⊢ Integrable (⇑L) μ

Tactic:
rw [← memLp_one_iff_integrable]

Goal state after:
E : Type u_1
inst✝⁴ : NormedAddCommGroup E
inst✝³ : NormedSpace ℝ E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsGaussian μ
L : StrongDual ℝ E
⊢ MemLp (⇑L) 1 μ
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: case e_v.e_a
μ : ℝ
v : ℝ≥0
L : ℝ →ₗ[ℝ] ℝ
this : ⇑L = fun x ↦ L 1 * x
⊢ L 1 ^ 2 = max ((fun x ↦ L 1 * x) 1 ^ 2) 0

Tactic:
simp only [mul_one, left_eq_sup]

Goal state after:
case e_v.e_a
μ : ℝ
v : ℝ≥0
L : ℝ →ₗ[ℝ] ℝ
this : ⇑L = fun x ↦ L 1 * x
⊢ 0 ≤ L 1 ^ 2
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: μ : ℝ
v : ℝ≥0
L : ℝ →ₗ[ℝ] ℝ
⊢ ∫ (x : ℝ), L x ∂gaussianReal μ v = L μ

Tactic:
have : ∫ x, L x ∂(gaussianReal μ v) = ∫ x, x ∂((gaussianReal μ v).map L) := by
    rw [integral_map (φ := L) (by fun_prop) (by fun_prop)]

Goal state after:
μ : ℝ
v : ℝ≥0
L : ℝ →ₗ[ℝ] ℝ
this : ∫ (x : ℝ), L x ∂gaussianReal μ v = ∫ (x : ℝ), x ∂Measure.map (⇑L) (gaussianReal μ v)
⊢ ∫ (x : ℝ), L x ∂gaussianReal μ v = L μ
Module: Mathlib.Probability.Distributions.Gaussian.Basic

Goal state before:
pp: case e_a.e_a
E : Type u_1
F : Type u_2
inst✝¹⁰ : NormedAddCommGroup E
inst✝⁹ : NormedSpace ℝ E
inst✝⁸ : MeasurableSpace E
inst✝⁷ : BorelSpace E
inst✝⁶ : NormedAddCommGroup F
inst✝⁵ : NormedSpace ℝ F
inst✝⁴ : MeasurableSpace F
inst✝³ : BorelSpace F
μ : Measure E
inst✝² : IsGaussian μ
inst✝¹ : SecondCountableTopologyEither E F
ν : Measure F
inst✝ : IsGaussian ν
L : StrongDual ℝ (E × F)
L₁ : E →L[ℝ] ℝ := L ∘SL ContinuousLinearMap.inl ℝ E F
hL₁ : L₁ = L ∘SL ContinuousLinearMap.inl ℝ E F
L₂ : F →L[ℝ] ℝ := L ∘SL ContinuousLinearMap.inr ℝ E F
hL₂ : L₂ = L ∘SL ContinuousLinearMap.inr ℝ E F
⊢ ↑(∫ (x : E), L₁ x ∂μ) + ↑(∫ (x : F), L₂ x ∂ν) = ↑(∫ (x : E × F), L x ∂μ.prod ν)

Tactic:
integral_continuousLinearMap_prod' (IsGaussian.integrable_dual μ (L.comp (.inl ℝ E F)))
      (IsGaussian.integrable_dual ν (L.comp (.inr ℝ E F)))

Goal state after:
case e_a.e_a
E : Type u_1
F : Type u_2
inst✝¹⁰ : NormedAddCommGroup E
inst✝⁹ : NormedSpace ℝ E
inst✝⁸ : MeasurableSpace E
inst✝⁷ : BorelSpace E
inst✝⁶ : NormedAddCommGroup F
inst✝⁵ : NormedSpace ℝ F
inst✝⁴ : MeasurableSpace F
inst✝³ : BorelSpace F
μ : Measure E
inst✝² : IsGaussian μ
inst✝¹ : SecondCountableTopologyEither E F
ν : Measure F
inst✝ : IsGaussian ν
L : StrongDual ℝ (E × F)
L₁ : E →L[ℝ] ℝ := L ∘SL ContinuousLinearMap.inl ℝ E F
hL₁ : L₁ = L ∘SL ContinuousLinearMap.inl ℝ E F
L₂ : F →L[ℝ] ℝ := L ∘SL ContinuousLinearMap.inr ℝ E F
hL₂ : L₂ = L ∘SL ContinuousLinearMap.inr ℝ E F
⊢ ↑(∫ (x : E), L₁ x ∂μ) + ↑(∫ (x : F), L₂ x ∂ν) =
    ↑(∫ (x : E), (L ∘SL ContinuousLinearMap.inl ℝ E F) x ∂μ + ∫ (y : F), (L ∘SL ContinuousLinearMap.inr ℝ E F) y ∂ν)
Module: Mathlib.Probability.Distributions.Fernique

Goal state before:
pp: case hb
c d : ℝ≥0∞
hc : 2⁻¹ < c
hd : d < 1
h : c ≤ d
⊢ d ≠ ∞

pp: case hb
c d : ℝ≥0∞
hc : 2⁻¹ < c
hd : d < 1
h : c ≤ d
⊢ 1 - c ≠ ∞

Tactic:
· finiteness

Goal state after:
case hb
c d : ℝ≥0∞
hc : 2⁻¹ < c
hd : d < 1
h : c ≤ d
⊢ 1 - c ≠ ∞
Module: Mathlib.Probability.Distributions.Fernique

Goal state before:
pp: case hb
c d : ℝ≥0∞
hc : 2⁻¹ < c
hd : d < 1
h : c ≤ d
⊢ 1 - c ≠ ∞

Tactic:
· finiteness

Goal state after:
Module: Mathlib.Probability.BrownianMotion.GaussianProjectiveFamily

Goal state before:
pp: I : Finset ℝ≥0
⊢ Measure.map (toLp 2) (projectiveFamily I) = multivariateGaussian 0 (covMatrix I)

Tactic:
by
    rw [projectiveFamily, Measure.map_map]
    · simp [← MeasurableEquiv.coe_toLp]
    all_goals fun_prop

Goal state after:
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
a : ℝ
inst✝ : IsProbabilityMeasure μ
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
ha_pos : 0 < a
c' : ℝ≥0∞
hc' : c' ≤ μ {x | ‖x‖ ≤ a}
hc'_gt : 2⁻¹ < c'
t : ℕ → ℝ := normThreshold a
c : ℝ≥0∞ := μ {x | ‖x‖ ≤ a}
C : ℝ := logRatio c' * a⁻¹ ^ 2
hc'_le : c' ≤ 1
ha : ∀ᵐ (a_1 : E) ∂μ, ‖a_1‖ ≤ a
ht_int_zero : ∫⁻ (x : E) in closedBall 0 a, ENNReal.ofReal (rexp (C * ‖x‖ ^ 2)) ∂μ ≤ ENNReal.ofReal (rexp (logRatio c'))
⊢ μ {x | x ∉ closedBall 0 a} = 0

Tactic:
← ae_iff

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
a : ℝ
inst✝ : IsProbabilityMeasure μ
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
ha_pos : 0 < a
c' : ℝ≥0∞
hc' : c' ≤ μ {x | ‖x‖ ≤ a}
hc'_gt : 2⁻¹ < c'
t : ℕ → ℝ := normThreshold a
c : ℝ≥0∞ := μ {x | ‖x‖ ≤ a}
C : ℝ := logRatio c' * a⁻¹ ^ 2
hc'_le : c' ≤ 1
ha : ∀ᵐ (a_1 : E) ∂μ, ‖a_1‖ ≤ a
ht_int_zero : ∫⁻ (x : E) in closedBall 0 a, ENNReal.ofReal (rexp (C * ‖x‖ ^ 2)) ∂μ ≤ ENNReal.ofReal (rexp (logRatio c'))
⊢ ∀ᵐ (a_1 : E) ∂μ, a_1 ∈ closedBall 0 a
Module: Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic

Goal state before:
pp: Ω : Type u_1
E : Type u_2
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝³ : NormedAddCommGroup E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
X : Ω → E
inst✝ : NormedSpace ℝ E
hX : HasGaussianLaw X P
⊢ HasGaussianLaw (-X) P

Tactic:
by simpa using hX.smul (-1)

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

Goal state before:
pp: case hb
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_pos : 0 < a
c' : ℝ≥0∞
hc' : c' ≤ μ {x | ‖x‖ ≤ a}
hc'_gt : 2⁻¹ < c'
t : ℕ → ℝ := ⋯
c : ℝ≥0∞ := ⋯
C : ℝ := ⋯
hc'_le : c' ≤ 1
ht_int_zero :
  ∫⁻ (x : E) in closedBall 0 a, ENNReal.ofReal (rexp (C * ‖x‖ ^ 2)) ∂μ ≤
    μ {x | ‖x‖ ≤ a} * ENNReal.ofReal (rexp (logRatio c'))
ha : ¬μ {x | ‖x‖ ≤ a} = 1
ha_lt : μ {x | ‖x‖ ≤ a} < 1
hc'_lt : c' < 1
h_iUnion : Set.univ = closedBall 0 a ∪ ⋃ n, closedBall 0 (t (n + 1)) \ closedBall 0 (t n)
this :
  ∫⁻ (x : E) in closedBall 0 (t 0) ∪ ⋃ n, closedBall 0 (t (n + 1)) \ closedBall 0 (t n),
      ENNReal.ofReal (rexp (C * ‖x‖ ^ 2)) ∂μ ≤
    ∫⁻ (x : E) in closedBall 0 (t 0), ENNReal.ofReal (rexp (C * ‖x‖ ^ 2)) ∂μ +
      ∑' (i : ℕ), ∫⁻ (x : E) in closedBall 0 (t (i + 1)) \ closedBall 0 (t i), ENNReal.ofReal (rexp (C * ‖x‖ ^ 2)) ∂μ
n : ℕ
⊢ 1 - c' ≠ ∞

Tactic:
· finiteness

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

Goal state before:
pp: 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
x y : ↥(I.disjSum J) → E
⊢ (fun s ↦ (x + y) ⟨Sum.inl ↑s, ⋯⟩, fun t ↦ (x + y) ⟨Sum.inr ↑t, ⋯⟩) =
    (fun s ↦ x ⟨Sum.inl ↑s, ⋯⟩, fun t ↦ x ⟨Sum.inr ↑t, ⋯⟩) + (fun s ↦ y ⟨Sum.inl ↑s, ⋯⟩, fun t ↦ y ⟨Sum.inr ↑t, ⋯⟩)

Tactic:
ext <;> simp

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

Goal state before:
pp: 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
c : ℝ
x : ↥(I.disjSum J) → E
⊢ (fun s ↦ (c • x) ⟨Sum.inl ↑s, ⋯⟩, fun t ↦ (c • x) ⟨Sum.inr ↑t, ⋯⟩) =
    (RingHom.id ℝ) c • (fun s ↦ x ⟨Sum.inl ↑s, ⋯⟩, fun t ↦ x ⟨Sum.inr ↑t, ⋯⟩)

Tactic:
ext <;> simp

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

Goal state before:
pp: case hv.hX
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)
⊢ ∀ (i : ↥I), MemLp (fun ω ↦ ω i) 2 (Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P)

Tactic:
exact fun i ↦ ((h1.hasGaussianLaw I).isGaussian_map.hasGaussianLaw_id.eval i).memLp_two

Goal state after:
Module: Mathlib.Probability.Kernel.Disintegration.CDFToKernel

Goal state before:
pp: α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
κ : Kernel α (β × ℝ)
ν : Kernel α β
f : α × β → ℚ → ℝ
hf : IsRatCondKernelCDFAux f κ ν
inst✝ : IsFiniteKernel ν
a : α
q : ℚ
⊢ Integrable (fun t ↦ ⨅ r, f (a, t) ↑r) (ν a)

Tactic:
rw [← memLp_one_iff_integrable]

Goal state after:
α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
κ : Kernel α (β × ℝ)
ν : Kernel α β
f : α × β → ℚ → ℝ
hf : IsRatCondKernelCDFAux f κ ν
inst✝ : IsFiniteKernel ν
a : α
q : ℚ
⊢ MemLp (fun t ↦ ⨅ r, f (a, t) ↑r) 1 (ν a)
Module: Mathlib.Probability.Kernel.Disintegration.CondCDF

Goal state before:
pp: α : Type u_1
mα : MeasurableSpace α
ρ : Measure (α × ℝ)
⊢ ∀ s ∈ range Iic, Measurable fun a ↦ (condCDF ρ a).measure s

Tactic:
by
    simp_rw [forall_mem_range, measure_condCDF_Iic]
    exact fun u ↦ (measurable_condCDF ρ u).ennreal_ofReal

Goal state after:
Module: Mathlib.Probability.CentralLimitTheorem

Goal state before:
pp: case inl.refine_1
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
P : Measure Ω
P' : Measure Ω'
X : ℕ → Ω → ℝ
Y : Ω' → ℝ
inst✝¹ : IsProbabilityMeasure P
inst✝ : IsProbabilityMeasure P'
hY : HasLaw Y (gaussianReal 0 Var[X 0; P].toNNReal) P'
hX : MemLp (X 0) 2 P
hindep : iIndepFun X P
hident : ∀ (i : ℕ), IdentDistrib (X i) (X 0) P P
h : Var[X 0; P] = 0
this : ∀ᵐ (ω : Ω) ∂P, ∀ (n : ℕ), X n ω = ∫ (x : Ω), X 0 x ∂P
mX : ∀ (n : ℕ), AEMeasurable (X n) P
n : ℕ
⊢ IdentDistrib (fun ω ↦ (√↑0)⁻¹ * (∑ k ∈ Finset.range 0, X k ω - ↑0 * ∫ (x : Ω), X 0 x ∂P))
    (fun ω ↦ (√↑n)⁻¹ * (∑ k ∈ Finset.range n, X k ω - ↑n * ∫ (x : Ω), X 0 x ∂P)) P P

Tactic:
refine ⟨by fun_prop, by fun_prop, Measure.map_congr ?_⟩
      filter_upwards [this] with ω hω
      simp [hω]

Goal state after:
Module: Mathlib.Probability.Kernel.Disintegration.Basic

Goal state before:
pp: case inl
α : Type u_1
Ω : Type u_3
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
ρ : Measure (α × Ω)
ρCond : Kernel α Ω
inst✝² : ρ.IsCondKernel ρCond
inst✝¹ : IsFiniteMeasure ρ
inst✝ : MeasurableSingletonClass α
s : Set Ω
hs : MeasurableSet s
this : IsSFiniteKernel ρCond
a : α
hx : ρ.fst {a} ≠ 0
⊢ (ρCond a) (Prod.mk a ⁻¹' {a} ×ˢ s) = {a}.indicator (fun x ↦ (ρCond x) s) a

Tactic:
simp only [singleton_prod, mem_singleton_iff, indicator_of_mem]

Goal state after:
case inl
α : Type u_1
Ω : Type u_3
mα : MeasurableSpace α
mΩ : MeasurableSpace Ω
ρ : Measure (α × Ω)
ρCond : Kernel α Ω
inst✝² : ρ.IsCondKernel ρCond
inst✝¹ : IsFiniteMeasure ρ
inst✝ : MeasurableSingletonClass α
s : Set Ω
hs : MeasurableSet s
this : IsSFiniteKernel ρCond
a : α
hx : ρ.fst {a} ≠ 0
⊢ (ρCond a) (Prod.mk a ⁻¹' Prod.mk a '' s) = (ρCond a) s
Module: Mathlib.Probability.Kernel.Disintegration.Density

Goal state before:
pp: α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
n : ℕ
a : α
s : Set β
hs : MeasurableSet s
⊢ Integrable (fun x ↦ κ.densityProcess ν n a x s) (ν a)

Tactic:
rw [← memLp_one_iff_integrable]

Goal state after:
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝¹ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
inst✝ : IsFiniteKernel ν
n : ℕ
a : α
s : Set β
hs : MeasurableSet s
⊢ MemLp (fun x ↦ κ.densityProcess ν n a x s) 1 (ν a)
Module: Mathlib.Probability.Kernel.Disintegration.Density

Goal state before:
pp: case neg
α : Type u_1
β : Type u_2
γ : Type u_3
mα : MeasurableSpace α
mβ : MeasurableSpace β
mγ : MeasurableSpace γ
inst✝ : CountablyGenerated γ
κ : Kernel α (γ × β)
ν : Kernel α γ
hκν : κ.fst ≤ ν
hν : IsFiniteKernel ν
n : ℕ
a : α
s : Set β
hs : MeasurableSet s
u : Set γ
hu : u ∈ countablePartition γ n
this : IsFiniteKernel κ
hu_meas : MeasurableSet u
x : γ
h0 : ¬(ν a) (countablePartitionSet n x) = 0
⊢ (κ a) (countablePartitionSet n x ×ˢ s) / (ν a) (countablePartitionSet n x) < ∞

Tactic:
· finiteness

Goal state after:
Module: Mathlib.Probability.Kernel.CondDistrib

Goal state before:
pp: α : Type u_1
β : Type u_2
Ω : Type u_3
inst✝³ : MeasurableSpace Ω
inst✝² : StandardBorelSpace Ω
inst✝¹ : Nonempty Ω
mα : MeasurableSpace α
μ : Measure α
inst✝ : IsFiniteMeasure μ
X : α → β
Y : α → Ω
mβ : MeasurableSpace β
hX : AEMeasurable X μ
hY : AEMeasurable Y μ
⊢ ⇑(condDistrib Y X μ) ∘ₘ Measure.map X μ = Measure.map Y μ

Tactic:
← Measure.snd_compProd,

Goal state after:
α : Type u_1
β : Type u_2
Ω : Type u_3
inst✝³ : MeasurableSpace Ω
inst✝² : StandardBorelSpace Ω
inst✝¹ : Nonempty Ω
mα : MeasurableSpace α
μ : Measure α
inst✝ : IsFiniteMeasure μ
X : α → β
Y : α → Ω
mβ : MeasurableSpace β
hX : AEMeasurable X μ
hY : AEMeasurable Y μ
⊢ (Measure.map X μ ⊗ₘ condDistrib Y X μ).snd = Measure.map Y μ