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

Goal state before:
pp: case neg
Ω : Type u_1
a b : ℝ
f : ℕ → Ω → ℝ
N : ℕ
ω : Ω
hf : a ≤ f N ω
hab : a < b
hN : ¬N = 0
⊢ (b - a) * ↑(upcrossingsBefore a b f N ω) ≤
    ∑ x ∈ Finset.range N,
      ∑ i ∈ Finset.range N,
        (Set.Ico (lowerCrossingTime a b f N i ω) (upperCrossingTime a b f N (i + 1) ω)).indicator 1 x *
          (f (x + 1) - f x) ω

Tactic:
←
    Set.indicator_mul_left _ _ (fun x ↦ (f (x + 1) - f x) ω),

Goal state after:
case neg
Ω : Type u_1
a b : ℝ
f : ℕ → Ω → ℝ
N : ℕ
ω : Ω
hf : a ≤ f N ω
hab : a < b
hN : ¬N = 0
⊢ (b - a) * ↑(upcrossingsBefore a b f N ω) ≤
    ∑ x ∈ Finset.range N,
      ∑ x_1 ∈ Finset.range N,
        (Set.Ico (lowerCrossingTime a b f N x_1 ω) (upperCrossingTime a b f N (x_1 + 1) ω)).indicator
          (fun j ↦ 1 j * (f (j + 1) - f j) ω) x
Module: Mathlib.Probability.Process.Stopping

Goal state before:
pp: case inl
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, ↑f (s m)
k : ℕ
x : Ω
h : ∀ (i : ℕ), s i ≤ s k → τ x < ↑(s i)
m : ℕ
hmk : s m ≤ s k
⊢ τ x < ↑(s m)

pp: case inr
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, ↑f (s m)
k : ℕ
x : Ω
h : ∀ (i : ℕ), s i ≤ s k → τ x < ↑(s i)
m : ℕ
hkm : s k ≤ s m
⊢ τ x < ↑(s m)

Tactic:
· exact h m hmk

Goal state after:
case inr
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, ↑f (s m)
k : ℕ
x : Ω
h : ∀ (i : ℕ), s i ≤ s k → τ x < ↑(s i)
m : ℕ
hkm : s k ≤ s m
⊢ τ x < ↑(s m)
Module: Mathlib.Probability.Martingale.Convergence

Goal state before:
pp: case pos
Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
ℱ : Filtration ℕ m0
a b : ℝ
f : ℕ → Ω → ℝ
R : ℝ≥0
inst✝ : IsFiniteMeasure μ
hf : Submartingale f ℱ μ
hbdd : ∀ (n : ℕ), eLpNorm (f n) 1 μ ≤ ↑R
hab : a < b
this : ∫⁻ (ω : Ω), upcrossings a b f ω ∂μ ≤ (⨆ N, ∫⁻ (ω : Ω), ENNReal.ofReal (f N ω - a)⁺ ∂μ) / ENNReal.ofReal (b - a)
hR' : ∀ (n : ℕ), ∫⁻ (ω : Ω), ↑‖f n ω - a‖₊ ∂μ ≤ ↑R + ↑‖a‖₊ * μ Set.univ
n : ℕ
ω : Ω
hnonneg : 0 ≤ f n ω - a
⊢ (f n ω - a)⁺ ≤ ‖f n ω - a‖

pp: case neg
Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
ℱ : Filtration ℕ m0
a b : ℝ
f : ℕ → Ω → ℝ
R : ℝ≥0
inst✝ : IsFiniteMeasure μ
hf : Submartingale f ℱ μ
hbdd : ∀ (n : ℕ), eLpNorm (f n) 1 μ ≤ ↑R
hab : a < b
this : ∫⁻ (ω : Ω), upcrossings a b f ω ∂μ ≤ (⨆ N, ∫⁻ (ω : Ω), ENNReal.ofReal (f N ω - a)⁺ ∂μ) / ENNReal.ofReal (b - a)
hR' : ∀ (n : ℕ), ∫⁻ (ω : Ω), ↑‖f n ω - a‖₊ ∂μ ≤ ↑R + ↑‖a‖₊ * μ Set.univ
n : ℕ
ω : Ω
hnonneg : f n ω - a < 0
⊢ (f n ω - a)⁺ ≤ ‖f n ω - a‖

Tactic:
· rw [posPart_eq_self.2 hnonneg, Real.norm_eq_abs, abs_of_nonneg hnonneg]

Goal state after:
case neg
Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
ℱ : Filtration ℕ m0
a b : ℝ
f : ℕ → Ω → ℝ
R : ℝ≥0
inst✝ : IsFiniteMeasure μ
hf : Submartingale f ℱ μ
hbdd : ∀ (n : ℕ), eLpNorm (f n) 1 μ ≤ ↑R
hab : a < b
this : ∫⁻ (ω : Ω), upcrossings a b f ω ∂μ ≤ (⨆ N, ∫⁻ (ω : Ω), ENNReal.ofReal (f N ω - a)⁺ ∂μ) / ENNReal.ofReal (b - a)
hR' : ∀ (n : ℕ), ∫⁻ (ω : Ω), ↑‖f n ω - a‖₊ ∂μ ≤ ↑R + ↑‖a‖₊ * μ Set.univ
n : ℕ
ω : Ω
hnonneg : f n ω - a < 0
⊢ (f n ω - a)⁺ ≤ ‖f n ω - a‖
Module: Mathlib.Probability.Martingale.Upcrossing

Goal state before:
pp: case pos
Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
f : ℕ → Ω → ℝ
ℱ : Filtration ℕ m0
inst✝ : IsFiniteMeasure μ
a b : ℝ
hf : Submartingale f ℱ μ
hab : a < b
⊢ ENNReal.ofReal (b - a) * ∫⁻ (ω : Ω), upcrossings a b f ω ∂μ ≤ ⨆ N, ∫⁻ (ω : Ω), ENNReal.ofReal (f N ω - a)⁺ ∂μ

Tactic:
upcrossings

Goal state after:
case pos
Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
f : ℕ → Ω → ℝ
ℱ : Filtration ℕ m0
inst✝ : IsFiniteMeasure μ
a b : ℝ
hf : Submartingale f ℱ μ
hab : a < b
⊢ ENNReal.ofReal (b - a) * ∫⁻ (ω : Ω), ⨆ N, ↑(upcrossingsBefore a b f N ω) ∂μ ≤
    ⨆ N, ∫⁻ (ω : Ω), ENNReal.ofReal (f N ω - a)⁺ ∂μ
Module: Mathlib.Probability.Martingale.Convergence

Goal state before:
pp: Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
ℱ : Filtration ℕ m0
inst✝ : IsFiniteMeasure μ
g : Ω → ℝ
⊢ Tendsto (fun n ↦ eLpNorm (μ[g | ↑ℱ n] - μ[g | ⨆ n, ↑ℱ n]) 1 μ) atTop (𝓝 0)

Tactic:
have ht : Tendsto (fun n => eLpNorm (μ[μ[g | ⨆ n, ℱ n] | ℱ n] - μ[g | ⨆ n, ℱ n]) 1 μ)
      atTop (𝓝 0) :=
    integrable_condExp.tendsto_eLpNorm_condExp stronglyMeasurable_condExp

Goal state after:
Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
ℱ : Filtration ℕ m0
inst✝ : IsFiniteMeasure μ
g : Ω → ℝ
ht : Tendsto (fun n ↦ eLpNorm (μ[μ[g | ⨆ n, ↑ℱ n] | ↑ℱ n] - μ[g | ⨆ n, ↑ℱ n]) 1 μ) atTop (𝓝 0)
⊢ Tendsto (fun n ↦ eLpNorm (μ[g | ↑ℱ n] - μ[g | ⨆ n, ↑ℱ n]) 1 μ) atTop (𝓝 0)
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
ω : Ω
hω : ((range (n + 1)).sup' ⋯ fun k ↦ f k ω) < ↑ε
⊢ hittingBtwn f {y | ↑ε ≤ y} 0 n ω = n

Tactic:
simp only [hittingBtwn, Set.mem_ofPred_eq, ite_eq_right_iff, forall_exists_index, and_imp]

Goal state after:
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
ω : Ω
hω : ((range (n + 1)).sup' ⋯ fun k ↦ f k ω) < ↑ε
⊢ ∀ x ∈ Set.Icc 0 n, ↑ε ≤ f x ω → sInf (Set.Icc 0 n ∩ {i | ↑ε ≤ f i ω}) = n
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
hB : B ∈ upperBounds (Set.range fun n ↦ ∑ x ∈ Finset.range n, μ.real (s (x + 1)))
n : ℕ
⊢ ¬↑B.toNNReal < ∑ k ∈ Finset.range n, μ (s (k + 1))

Tactic:
rw [mem_upperBounds] at hB

Goal state after:
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
hB : ∀ x ∈ Set.range fun n ↦ ∑ x ∈ Finset.range n, μ.real (s (x + 1)), x ≤ B
n : ℕ
⊢ ¬↑B.toNNReal < ∑ k ∈ Finset.range n, μ (s (k + 1))
Module: Mathlib.Probability.Martingale.BorelCantelli

Goal state before:
pp: Ω : Type u_2
s : ℕ → Set Ω
ω : Ω
n : ℕ
⊢ |process s (n + 1) ω - process s n ω| ≤ ↑1

Tactic:
by
  norm_cast
  rw [process, process, Finset.sum_apply, Finset.sum_apply,
    Finset.sum_range_succ_sub_sum, ← Real.norm_eq_abs, norm_indicator_eq_indicator_norm]
  refine Set.indicator_le' (fun _ _ => ?_) (fun _ _ => zero_le_one) _
  rw [Pi.one_apply, norm_one]

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

Goal state before:
pp: case succ
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
n : ℕ
hn :
  ∀ {t : ℝ},
    t ∈ interior (integrableExpSet X μ) →
      HasDerivAt (iteratedDeriv n (mgf X μ)) (∫ (x : Ω), (fun ω ↦ X ω ^ (n + 1) * rexp (t * X ω)) x ∂μ) t
t : ℝ
ht : t ∈ interior (integrableExpSet X μ)
this : deriv (iteratedDeriv n (mgf X μ)) =ᶠ[𝓝 t] fun t ↦ ∫ (x : Ω), (fun ω ↦ X ω ^ (n + 1) * rexp (t * X ω)) x ∂μ
⊢ HasDerivAt (fun t ↦ ∫ (x : Ω), (fun ω ↦ X ω ^ (n + 1) * rexp (t * X ω)) x ∂μ)
    (∫ (x : Ω), (fun ω ↦ X ω ^ (n + 1 + 1) * rexp (t * X ω)) x ∂μ) t

Tactic:
exact hasDerivAt_integral_pow_mul_exp_real ht (n + 1)

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

Goal state before:
pp: case pos
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
v : ℝ
h : v ∈ interior (integrableExpSet X μ)
hμ : μ = 0
⊢ deriv (cgf X μ) v = (∫ (x : Ω), (fun ω ↦ X ω * rexp (v * X ω)) x ∂μ) / mgf X μ v

pp: case neg
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
v : ℝ
h : v ∈ interior (integrableExpSet X μ)
hμ : ¬μ = 0
⊢ deriv (cgf X μ) v = (∫ (x : Ω), (fun ω ↦ X ω * rexp (v * X ω)) x ∂μ) / mgf X μ v

Tactic:
· simp only [hμ, cgf_zero_measure, integral_zero_measure, mgf_zero_measure, div_zero,
      Pi.zero_apply]
    exact deriv_const v 0

Goal state after:
case neg
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
v : ℝ
h : v ∈ interior (integrableExpSet X μ)
hμ : ¬μ = 0
⊢ deriv (cgf X μ) v = (∫ (x : Ω), (fun ω ↦ X ω * rexp (v * X ω)) x ∂μ) / mgf X μ v
Module: Mathlib.Probability.Moments.MGFAnalytic

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
v : ℝ
h : v ∈ interior (integrableExpSet X μ)
hμ : ¬μ = 0
h_mem : ∀ᶠ (y : ℝ) in 𝓝 v, y ∈ interior (integrableExpSet X μ)
⊢ deriv (cgf X μ) =ᶠ[𝓝 v] fun u ↦ (∫ (x : Ω), (fun ω ↦ X ω * rexp (u * X ω)) x ∂μ) / mgf X μ u

Tactic:
filter_upwards [h_mem] with u hu using deriv_cgf hu

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

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

Tactic:
induction n generalizing z with

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

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

Tactic:
induction n generalizing z with

Goal state after:
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
h_mem : ∀ᶠ (y : ℝ) in 𝓝 v, y ∈ interior (integrableExpSet X μ)
h_d_cgf : deriv (cgf X μ) =ᶠ[𝓝 v] fun u ↦ (∫ (x : Ω), (fun ω ↦ X ω * rexp (u * X ω)) x ∂μ) / mgf X μ u
h_d_mgf : deriv (mgf X μ) =ᶠ[𝓝 v] fun u ↦ ∫ (x : Ω), (fun ω ↦ X ω * rexp (u * X ω)) x ∂μ
⊢ ((∫ (ω : Ω), X ω * rexp (v * X ω) ∂μ) * ∫ (ω : Ω), X ω * rexp (v * X ω) ∂μ) / mgf X μ v ^ 2 = deriv (cgf X μ) v ^ 2

Tactic:
· rw [deriv_cgf h]
      ring

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

Goal state before:
pp: E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : InnerProductSpace ℝ E
inst✝¹ : MeasurableSpace E
inst✝ : BorelSpace E
μ : Measure E
h : ¬MemLp id 2 μ
⊢ covarianceBilin μ = 0

Tactic:
ext
  simp [covarianceBilin_eq_covarianceBilinDual, h]

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

Goal state before:
pp: case neg
E : Type u_1
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : InnerProductSpace ℝ E
inst✝³ : MeasurableSpace E
inst✝² : BorelSpace E
μ : Measure E
inst✝¹ : CompleteSpace E
inst✝ : IsProbabilityMeasure μ
c x✝¹ x✝ : E
h : MemLp (id ∘ fun x ↦ c + x) 2 μ
⊢ MemLp id 2 μ

Tactic:
convert! (memLp_const (-c)).add h

Goal state after:
E : Type u_1
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : InnerProductSpace ℝ E
inst✝³ : MeasurableSpace E
inst✝² : BorelSpace E
μ : Measure E
inst✝¹ : CompleteSpace E
inst✝ : IsProbabilityMeasure μ
c x✝¹ x✝ : E
h : MemLp (id ∘ fun x ↦ c + x) 2 μ
⊢ id = (fun x ↦ -c) + id ∘ fun x ↦ c + x
Module: Mathlib.Probability.Distributions.Fernique

Goal state before:
pp: case e_6.e'_1
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✝ : SFinite μ
h : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
a b : ℝ
h_twos : ‖2⁻¹ * √2‖ = (√2)⁻¹
p : E × E
⊢ ‖(√2 / 2) • p.1 + -((√2 / 2) • p.2)‖ = ‖p.1 - p.2‖ / √2

pp: case e_6.e'_2
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✝ : SFinite μ
h : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
a b : ℝ
h_twos : ‖2⁻¹ * √2‖ = (√2)⁻¹
p : E × E
⊢ ‖(√2 / 2) • p.1 + (√2 / 2) • p.2‖ = ‖p.1 + p.2‖ / √2

Tactic:
· rw [← sub_eq_add_neg, ← smul_sub, norm_smul, div_eq_inv_mul, div_eq_inv_mul, h_twos]

Goal state after:
case e_6.e'_2
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✝ : SFinite μ
h : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
a b : ℝ
h_twos : ‖2⁻¹ * √2‖ = (√2)⁻¹
p : E × E
⊢ ‖(√2 / 2) • p.1 + (√2 / 2) • p.2‖ = ‖p.1 + p.2‖ / √2
Module: Mathlib.Probability.Distributions.Gaussian.Basic

Goal state before:
pp: case 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
⊢ ↑Var[⇑L₁; μ] / 2 + ↑Var[⇑L₂; ν] / 2 = ↑Var[⇑L; μ.prod ν] / 2

Tactic:
field_simp

Goal state after:
case 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
⊢ ↑Var[⇑L₁; μ] + ↑Var[⇑L₂; ν] = ↑Var[⇑L; μ.prod ν]
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✝ : SFinite μ
h : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
a b : ℝ
p : E × E
hp1 : ‖p.1 - p.2‖ / √2 ≤ a
hp2 : b < ‖p.1 + p.2‖ / √2
this : b - a < ‖p.1 + p.2‖ / √2 - ‖p.1 - p.2‖ / √2
⊢ (‖p.1 + p.2‖ / √2 - ‖p.1 - p.2‖ / √2) / √2 = (‖p.1 + p.2‖ - ‖p.1 - p.2‖) / 2

Tactic:
field_simp

Goal state after:
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✝ : SFinite μ
h : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
a b : ℝ
p : E × E
hp1 : ‖p.1 - p.2‖ / √2 ≤ a
hp2 : b < ‖p.1 + p.2‖ / √2
this : b - a < ‖p.1 + p.2‖ / √2 - ‖p.1 - p.2‖ / √2
⊢ (‖p.1 + p.2‖ - ‖p.1 - p.2‖) * 2 = √2 ^ 2 * (‖p.1 + p.2‖ - ‖p.1 - p.2‖)
Module: Mathlib.Probability.Distributions.Fernique

Goal state before:
pp: a : ℝ
n : ℕ
⊢ (√2 ^ (n + 2) - 1) ^ 2 ≤ (√2 ^ (n + 2)) ^ 2

Tactic:
by
    gcongr
    · calc 0 ≤ √2 ^ (0 + 2) - 1 := by simp
      _ ≤ √2 ^ (n + 2) - 1 := by gcongr <;> simp
    · exact sub_le_self _ (by simp)

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

Goal state before:
pp: E : Type u_1
inst✝⁶ : NormedAddCommGroup E
inst✝⁵ : SecondCountableTopology E
inst✝⁴ : CompleteSpace E
inst✝³ : MeasurableSpace E
inst✝² : BorelSpace E
μ : Measure E
inst✝¹ : NormedSpace ℝ E
inst✝ : IsFiniteMeasure μ
m : E
f : StrongDual ℝ E →L[ℝ] StrongDual ℝ E →L[ℝ] ℝ
hf : f.toBilinForm.IsPosSemidef
h' : IsGaussian μ
n : StrongDual ℝ E → ℤ
hn :
  ∀ (L : StrongDual ℝ E),
    ↑(L (∫ (x : E), id x ∂μ)) * I - ↑(((covarianceBilinDual μ) L) L) / 2 =
      ↑(L m) * I - ↑((f L) L) / 2 + ↑(n L) * (2 * ↑π * I)
L : StrongDual ℝ E
⊢ ↑(n L) = (↑(L m) * I - ↑((f L) L) / 2 + ↑(n L) * (2 * ↑π * I) - ↑(L m) * I + ↑((f L) L) / 2) / (2 * ↑π * I)

Tactic:
field_simp

Goal state after:
E : Type u_1
inst✝⁶ : NormedAddCommGroup E
inst✝⁵ : SecondCountableTopology E
inst✝⁴ : CompleteSpace E
inst✝³ : MeasurableSpace E
inst✝² : BorelSpace E
μ : Measure E
inst✝¹ : NormedSpace ℝ E
inst✝ : IsFiniteMeasure μ
m : E
f : StrongDual ℝ E →L[ℝ] StrongDual ℝ E →L[ℝ] ℝ
hf : f.toBilinForm.IsPosSemidef
h' : IsGaussian μ
n : StrongDual ℝ E → ℤ
hn :
  ∀ (L : StrongDual ℝ E),
    ↑(L (∫ (x : E), id x ∂μ)) * I - ↑(((covarianceBilinDual μ) L) L) / 2 =
      ↑(L m) * I - ↑((f L) L) / 2 + ↑(n L) * (2 * ↑π * I)
L : StrongDual ℝ E
⊢ ↑(n L) * I * 2 ^ 2 * ↑π = ↑(L m) * I * 2 - ↑((f L) L) + ↑(n L) * I * 2 ^ 2 * ↑π - ↑(L m) * I * 2 + ↑((f L) L)
Module: Mathlib.Probability.Distributions.Gaussian.CharFun

Goal state before:
pp: case right
E : Type u_1
inst✝⁶ : NormedAddCommGroup E
inst✝⁵ : SecondCountableTopology E
inst✝⁴ : CompleteSpace E
inst✝³ : MeasurableSpace E
inst✝² : BorelSpace E
μ : Measure E
inst✝¹ : NormedSpace ℝ E
inst✝ : IsFiniteMeasure μ
m : E
f : StrongDual ℝ E →L[ℝ] StrongDual ℝ E →L[ℝ] ℝ
hf : f.toBilinForm.IsPosSemidef
h' : IsGaussian μ
n : StrongDual ℝ E → ℤ
h :
  ∀ (L : StrongDual ℝ E),
    ↑(n L) =
      (↑(L (∫ (x : E), id x ∂μ)) * I - ↑(((covarianceBilinDual μ) L) L) / 2 - ↑(L m) * I + ↑((f L) L) / 2) /
        (2 * ↑π * I)
this✝¹ : Continuous n
this✝ : ∀ (x : StrongDual ℝ E), n = Function.const (StrongDual ℝ E) (n x)
this : ∀ (L : StrongDual ℝ E), n L = 0
hn : ∀ (L : StrongDual ℝ E), ((covarianceBilinDual μ) L) L = (f L) L ∧ L (∫ (x : E), x ∂μ) = L m
⊢ f = covarianceBilinDual μ

Tactic:
rw [← toBilinForm_inj]
    apply LinearMap.BilinForm.ext_of_isSymm hf.isSymm isPosSemidef_covarianceBilinDual.isSymm
    intro x
    simp [covarianceBilinDual_self_eq_variance IsGaussian.memLp_two_id, (hn x).1.symm]

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

Goal state before:
pp: case pos
a : ℝ
c : ℝ≥0∞
hc_gt : 2⁻¹ < c
hc_lt : c < 1
n : ℕ
ha : a = 0
⊢ (1 - c).toReal ≤ c.toReal

pp: case pos
a : ℝ
c : ℝ≥0∞
hc_gt : 2⁻¹ < c
hc_lt : c < 1
n : ℕ
ha : a = 0
⊢ 0 < (1 - c).toReal

Tactic:
· refine (ENNReal.toReal_le_toReal (by finiteness) (by finiteness)).mpr ?_
      refine tsub_le_iff_left.mpr ?_
      rw [← two_mul]
      rw [inv_eq_one_div, ENNReal.div_lt_iff (by simp) (by simp), mul_comm] at hc_gt
      exact hc_gt.le

Goal state after:
case pos
a : ℝ
c : ℝ≥0∞
hc_gt : 2⁻¹ < c
hc_lt : c < 1
n : ℕ
ha : a = 0
⊢ 0 < (1 - c).toReal
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
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
⊢ μ (closedBall 0 a) * ENNReal.ofReal (rexp (logRatio c' * (a ^ 2)⁻¹ * a ^ 2)) =
    μ {x | ‖x‖ ≤ a} * ENNReal.ofReal (rexp (logRatio c'))

Tactic:
field_simp

Goal state after:
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
⊢ μ (closedBall 0 a) * ENNReal.ofReal (rexp (logRatio c')) = μ {x | ‖x‖ ≤ a} * ENNReal.ofReal (rexp (logRatio c'))
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'))
⊢ Set.univ =ᵐ[μ] closedBall 0 a

Tactic:
rw [eventuallyEq_comm, ae_eq_univ]
      change μ {x | ¬ x ∈ closedBall 0 a} = 0
      rw [← ae_iff]
      filter_upwards [ha] with x hx using by simp [hx]

Goal state after: