mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.Order.Std

Goal state before:
pp: case isTrue.isTrue
α : Type u_1
args : OfStdArgs α
this✝⁵ : LE α := args.le
this✝⁴ : LT α := args.lt
this✝³ :
  let this := args.le;
  let this_1 := args.lt;
  Std.LawfulOrderLT α
this✝² :
  let this := args.le;
  Std.IsLinearOrder α
this✝¹ : DecidableLE α := args.decidableLE
this✝ : Std.LawfulOrderLeftLeaningMin α
this : Std.LawfulOrderLeftLeaningMax α
a b : α
h✝¹ : b ≤ a
h✝ : a ≤ b
⊢ a = b

pp: case isTrue.isFalse
α : Type u_1
args : OfStdArgs α
this✝⁵ : LE α := args.le
this✝⁴ : LT α := args.lt
this✝³ :
  let this := args.le;
  let this_1 := args.lt;
  Std.LawfulOrderLT α
this✝² :
  let this := args.le;
  Std.IsLinearOrder α
this✝¹ : DecidableLE α := args.decidableLE
this✝ : Std.LawfulOrderLeftLeaningMin α
this : Std.LawfulOrderLeftLeaningMax α
a b : α
h✝¹ : b ≤ a
h✝ : ¬a ≤ b
⊢ a = a

Tactic:
· exact Std.le_antisymm ‹_› ‹_›

Goal state after:
case isTrue.isFalse
α : Type u_1
args : OfStdArgs α
this✝⁵ : LE α := args.le
this✝⁴ : LT α := args.lt
this✝³ :
  let this := args.le;
  let this_1 := args.lt;
  Std.LawfulOrderLT α
this✝² :
  let this := args.le;
  Std.IsLinearOrder α
this✝¹ : DecidableLE α := args.decidableLE
this✝ : Std.LawfulOrderLeftLeaningMin α
this : Std.LawfulOrderLeftLeaningMax α
a b : α
h✝¹ : b ≤ a
h✝ : ¬a ≤ b
⊢ a = a
Module: Mathlib.Order.Partition.Basic

Goal state before:
pp: case pos
α : Type u_1
t u : Set α
P : Partition u
f₀ : ↑t → α
h_notMem : ∀ (x : ↑t), ↑x ∉ u → f₀ x = ↑x
h_mem : ∀ (x : ↑t), ↑x ∈ u → P.Rel (↑x) (f₀ x)
h_eq : ∀ (x y : ↑t), P.Rel ↑x ↑y → f₀ x = f₀ y
f : α → α := fun a ↦ if ha : a ∈ u then if hb : ∃ b, P.Rel a ↑b then f₀ hb.choose else P.rep ⋯ else a
hfdef : f = fun a ↦ if ha : a ∈ u then if hb : ∃ b, P.Rel a ↑b then f₀ hb.choose else P.rep ⋯ else a
a : ↑t
ha : ↑a ∈ u
h : ∃ b, P.Rel ↑a ↑b
⊢ f₀ h.choose = f₀ a

pp: case neg
α : Type u_1
t u : Set α
P : Partition u
f₀ : ↑t → α
h_notMem : ∀ (x : ↑t), ↑x ∉ u → f₀ x = ↑x
h_mem : ∀ (x : ↑t), ↑x ∈ u → P.Rel (↑x) (f₀ x)
h_eq : ∀ (x y : ↑t), P.Rel ↑x ↑y → f₀ x = f₀ y
f : α → α := fun a ↦ if ha : a ∈ u then if hb : ∃ b, P.Rel a ↑b then f₀ hb.choose else P.rep ⋯ else a
hfdef : f = fun a ↦ if ha : a ∈ u then if hb : ∃ b, P.Rel a ↑b then f₀ hb.choose else P.rep ⋯ else a
a : ↑t
ha : ↑a ∈ u
h : ¬∃ b, P.Rel ↑a ↑b
⊢ P.rep ⋯ = f₀ a

Tactic:
· exact h_eq _ _ h.choose_spec |>.symm

Goal state after:
case neg
α : Type u_1
t u : Set α
P : Partition u
f₀ : ↑t → α
h_notMem : ∀ (x : ↑t), ↑x ∉ u → f₀ x = ↑x
h_mem : ∀ (x : ↑t), ↑x ∈ u → P.Rel (↑x) (f₀ x)
h_eq : ∀ (x y : ↑t), P.Rel ↑x ↑y → f₀ x = f₀ y
f : α → α := fun a ↦ if ha : a ∈ u then if hb : ∃ b, P.Rel a ↑b then f₀ hb.choose else P.rep ⋯ else a
hfdef : f = fun a ↦ if ha : a ∈ u then if hb : ∃ b, P.Rel a ↑b then f₀ hb.choose else P.rep ⋯ else a
a : ↑t
ha : ↑a ∈ u
h : ¬∃ b, P.Rel ↑a ↑b
⊢ P.rep ⋯ = f₀ a
Module: Mathlib.Order.SuccPred.Tree

Goal state before:
pp: t : RootedTree
t₁ : SubRootedTree t
ht₁ : t₁ ∈ t.subtrees
t₂ : SubRootedTree t
ht₂ : t₂ ∈ t.subtrees
h : t₁ ≠ t₂
⊢ ∀ ⦃a : ↑t⦄, a ∈ ↑t₁ → a ∉ ↑t₂

Tactic:
intro a ha hb

Goal state after:
t : RootedTree
t₁ : SubRootedTree t
ht₁ : t₁ ∈ t.subtrees
t₂ : SubRootedTree t
ht₂ : t₂ ∈ t.subtrees
h : t₁ ≠ t₂
a : ↑t
ha : a ∈ ↑t₁
hb : a ∈ ↑t₂
⊢ False
Module: Mathlib.Probability.Martingale.Basic

Goal state before:
pp: Ω : Type u_1
E : Type u_2
ι : Type u_3
inst✝⁸ : Preorder ι
m0 : MeasurableSpace Ω
μ : Measure Ω
inst✝⁷ : NormedAddCommGroup E
inst✝⁶ : NormedSpace ℝ E
ℱ : Filtration ι m0
inst✝⁵ : CompleteSpace E
inst✝⁴ : PartialOrder E
inst✝³ : IsOrderedAddMonoid E
inst✝² : IsOrderedModule ℝ E
inst✝¹ : ClosedIciTopology E
inst✝ : SigmaFiniteFiltration μ ℱ
f : ι → Ω → E
hf : Submartingale f ℱ μ
i j : ι
hij : i ≤ j
s : Set Ω
hs : MeasurableSet s
⊢ ∫ (ω : Ω) in s, f i ω ∂μ ≤ ∫ (ω : Ω) in s, f j ω ∂μ

Tactic:
rw [← neg_le_neg_iff, ← integral_neg, ← integral_neg]

Goal state after:
Ω : Type u_1
E : Type u_2
ι : Type u_3
inst✝⁸ : Preorder ι
m0 : MeasurableSpace Ω
μ : Measure Ω
inst✝⁷ : NormedAddCommGroup E
inst✝⁶ : NormedSpace ℝ E
ℱ : Filtration ι m0
inst✝⁵ : CompleteSpace E
inst✝⁴ : PartialOrder E
inst✝³ : IsOrderedAddMonoid E
inst✝² : IsOrderedModule ℝ E
inst✝¹ : ClosedIciTopology E
inst✝ : SigmaFiniteFiltration μ ℱ
f : ι → Ω → E
hf : Submartingale f ℱ μ
i j : ι
hij : i ≤ j
s : Set Ω
hs : MeasurableSet s
⊢ ∫ (a : Ω) in s, -f j a ∂μ ≤ ∫ (a : Ω) in s, -f i a ∂μ
Module: Mathlib.Probability.Martingale.Centering

Goal state before:
pp: Ω : Type u_1
E : Type u_2
m0 : MeasurableSpace Ω
μ : Measure Ω
inst✝² : NormedAddCommGroup E
inst✝¹ : NormedSpace ℝ E
inst✝ : CompleteSpace E
R : ℝ
f : ℕ → Ω → E
ℱ : Filtration ℕ m0
hbdd : ∀ᵐ (ω : Ω) ∂μ, ∀ (i : ℕ), ‖f (i + 1) ω - f i ω‖ ≤ R
⊢ ∀ᵐ (ω : Ω) ∂μ, ∀ (i : ℕ), ‖μ[f (i + 1) - f i | ↑ℱ i] ω‖ ≤ R

Tactic:
exact ae_all_iff.2 fun i => ae_bdd_norm_condExp_of_ae_bdd_norm <| ae_all_iff.1 hbdd i

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

Goal state before:
pp: case h.inr
Ω : Type u_1
a b : ℝ
f : ℕ → Ω → ℝ
N n : ℕ
ω : Ω
i : ℕ
a✝¹ : i ∈ ↑(Finset.range N)
j : ℕ
a✝ : j ∈ ↑(Finset.range N)
hij : i ≠ j
hij' : j < i
⊢ min (upperCrossingTime a b f N (i + 1) ω) (upperCrossingTime a b f N (j + 1) ω) ≤
    max (lowerCrossingTime a b f N i ω) (lowerCrossingTime a b f N j ω)

Tactic:
rw [min_eq_right (upperCrossingTime_mono (Nat.succ_le_succ hij'.le) :
      upperCrossingTime a b f N _ ω ≤ upperCrossingTime a b f N _ ω),
      max_eq_left (lowerCrossingTime_mono hij'.le :
        lowerCrossingTime a b f N _ _ ≤ lowerCrossingTime _ _ _ _ _ _)]

Goal state after:
case h.inr
Ω : Type u_1
a b : ℝ
f : ℕ → Ω → ℝ
N n : ℕ
ω : Ω
i : ℕ
a✝¹ : i ∈ ↑(Finset.range N)
j : ℕ
a✝ : j ∈ ↑(Finset.range N)
hij : i ≠ j
hij' : j < i
⊢ upperCrossingTime a b f N j.succ ω ≤ lowerCrossingTime a b f N i ω
Module: Mathlib.Probability.Martingale.Basic

Goal state before:
pp: Ω : Type u_1
E : Type u_2
m0 : MeasurableSpace Ω
μ : Measure Ω
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
𝒢 : Filtration ℕ m0
inst✝¹ : CompleteSpace E
inst✝ : SigmaFiniteFiltration μ 𝒢
f : ℕ → Ω → E
hfmgle : Martingale f 𝒢 μ
hfadp : StronglyAdapted 𝒢 fun n ↦ f (n + 1)
n : ℕ
⊢ f n =ᵐ[μ] f 0

Tactic:
induction n with
  | zero => rfl
  | succ k ih =>
    exact ((Germ.coe_eq.mp (congr_arg Germ.ofFun <| condExp_of_stronglyMeasurable (𝒢.le _) (hfadp _)
      (hfmgle.integrable _))).symm.trans (hfmgle.2 k (k + 1) k.le_succ)).trans ih

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

Goal state before:
pp: Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝⁶ : ConditionallyCompleteLinearOrderBot ι
inst✝⁵ : TopologicalSpace ι
inst✝⁴ : OrderTopology ι
inst✝³ : DenselyOrdered ι
inst✝² : FirstCountableTopology ι
inst✝¹ : NoMaxOrder ι
κ : Type u_4
f : Filtration ι m
τ : κ → Ω → WithTop ι
s : Set κ
hs : s.Countable
inst✝ : f.IsRightContinuous
hτ : ∀ n ∈ s, IsStoppingTime f (τ n)
⊢ IsStoppingTime f fun ω ↦ ⨅ n ∈ s, τ n ω

Tactic:
refine isStoppingTime_of_measurableSet_lt_of_isRightContinuous <|
    fun i ↦ MeasurableSet.of_compl ?_

Goal state after:
Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝⁶ : ConditionallyCompleteLinearOrderBot ι
inst✝⁵ : TopologicalSpace ι
inst✝⁴ : OrderTopology ι
inst✝³ : DenselyOrdered ι
inst✝² : FirstCountableTopology ι
inst✝¹ : NoMaxOrder ι
κ : Type u_4
f : Filtration ι m
τ : κ → Ω → WithTop ι
s : Set κ
hs : s.Countable
inst✝ : f.IsRightContinuous
hτ : ∀ n ∈ s, IsStoppingTime f (τ n)
i : ι
⊢ MeasurableSet {ω | ⨅ n ∈ s, τ n ω < ↑i}ᶜ
Module: Mathlib.Probability.Process.Stopping

Goal state before:
pp: case refine_1
Ω : Type u_1
β : Type u_2
ι : Type u_3
m : MeasurableSpace Ω
inst✝ : Preorder ι
f : Filtration ι m
τ π : Ω → WithTop ι
hτ : IsStoppingTime f τ
s : Set Ω
hs : MeasurableSet s ∧ ∀ (i : ι), MeasurableSet (s ∩ {ω | τ ω ≤ ↑i})
i : ι
⊢ MeasurableSet (sᶜ ∪ {ω | τ ω ≤ ↑i}ᶜ)

pp: case refine_2
Ω : Type u_1
β : Type u_2
ι : Type u_3
m : MeasurableSpace Ω
inst✝ : Preorder ι
f : Filtration ι m
τ π : Ω → WithTop ι
hτ : IsStoppingTime f τ
s : Set Ω
hs : MeasurableSet s ∧ ∀ (i : ι), MeasurableSet (s ∩ {ω | τ ω ≤ ↑i})
i : ι
⊢ MeasurableSet {ω | τ ω ≤ ↑i}

Tactic:
· rw [← Set.compl_inter]
        exact (hs.2 i).compl

Goal state after:
case refine_2
Ω : Type u_1
β : Type u_2
ι : Type u_3
m : MeasurableSpace Ω
inst✝ : Preorder ι
f : Filtration ι m
τ π : Ω → WithTop ι
hτ : IsStoppingTime f τ
s : Set Ω
hs : MeasurableSet s ∧ ∀ (i : ι), MeasurableSet (s ∩ {ω | τ ω ≤ ↑i})
i : ι
⊢ MeasurableSet {ω | τ ω ≤ ↑i}
Module: Mathlib.Probability.Process.Stopping

Goal state before:
pp: case coe
Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝³ : LinearOrder ι
f : Filtration ι m
τ : Ω → WithTop ι
inst✝² : TopologicalSpace ι
inst✝¹ : OrderTopology ι
inst✝ : FirstCountableTopology ι
hτ : IsStoppingTime f τ
i : ι
ω : Ω
a : ι
⊢ ↑i < ↑a ∨ ↑i = ↑a ↔ ↑a = ↑i ∨ ↑i < ↑a

Tactic:
norm_cast
      rw [@eq_comm _ i, or_comm]

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

Goal state before:
pp: case coe
Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝ : LinearOrder ι
f : Filtration ι m
τ : Ω → WithTop ι
hτ : IsStoppingTime f τ
h_countable : (Set.range τ).Countable
i : ι
ω : Ω
a : ι
⊢ ↑i < ↑a ∨ ↑i = ↑a ↔ ↑a = ↑i ∨ ↑i < ↑a

Tactic:
norm_cast
      rw [@eq_comm _ i, or_comm]

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

Goal state before:
pp: Ω : Type u_2
m0 : MeasurableSpace Ω
μ : Measure Ω
ℱ : Filtration ℕ m0
f : ℕ → Ω → ℝ
R : ℝ≥0
inst✝ : IsFiniteMeasure μ
hf : Submartingale f ℱ μ
hf0 : f 0 = 0
hbdd : ∀ᵐ (ω : Ω) ∂μ, ∀ (i : ℕ), |f (i + 1) ω - f i ω| ≤ ↑R
ht : ∀ᵐ (ω : Ω) ∂μ, ∀ (i : ℕ), ∃ c, Tendsto (fun n ↦ stoppedAbove f (↑i) n ω) atTop (𝓝 c)
ω : Ω
hω : ∀ (i : ℕ), ∃ c, Tendsto (fun n ↦ stoppedAbove f (↑i) n ω) atTop (𝓝 c)
⊢ BddAbove (Set.range fun n ↦ f n ω) → ∃ c, Tendsto (fun n ↦ f n ω) atTop (𝓝 c)

Tactic:
hωb

Goal state after:
Ω : Type u_2
m0 : MeasurableSpace Ω
μ : Measure Ω
ℱ : Filtration ℕ m0
f : ℕ → Ω → ℝ
R : ℝ≥0
inst✝ : IsFiniteMeasure μ
hf : Submartingale f ℱ μ
hf0 : f 0 = 0
hbdd : ∀ᵐ (ω : Ω) ∂μ, ∀ (i : ℕ), |f (i + 1) ω - f i ω| ≤ ↑R
ht : ∀ᵐ (ω : Ω) ∂μ, ∀ (i : ℕ), ∃ c, Tendsto (fun n ↦ stoppedAbove f (↑i) n ω) atTop (𝓝 c)
ω : Ω
hω : ∀ (i : ℕ), ∃ c, Tendsto (fun n ↦ stoppedAbove f (↑i) n ω) atTop (𝓝 c)
hωb : BddAbove (Set.range fun n ↦ f n ω)
⊢ ∃ c, Tendsto (fun n ↦ f n ω) atTop (𝓝 c)
Module: Mathlib.Probability.Moments.Basic

Goal state before:
pp: case neg
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
inst✝ : IsZeroOrProbabilityMeasure μ
h : IsProbabilityMeasure μ
h_int : ¬Integrable X μ
⊢ centralMoment X 1 μ = 0

Tactic:
· simp only [centralMoment, Pi.sub_apply, pow_one]
    have : ¬Integrable (fun x => X x - integral μ X) μ := by
      refine fun h_sub => h_int ?_
      have h_add : X = (fun x => X x - integral μ X) + fun _ => integral μ X := by ext1 x; simp
      rw [h_add]
      fun_prop
    rw [integral_undef this]

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

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t : ℝ
⊢ cgf (-X) μ t = cgf X μ (-t)

Tactic:
simp_rw [cgf, mgf_neg]

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

Goal state before:
pp: Ω : Type u_1
ι : Type u_3
inst✝¹ : Nonempty ι
τ : Ω → WithTop ι
E : Type u_4
u : ι → Ω → E
inst✝ : AddCommMonoid E
s : Finset ι
hbdd : ∀ (ω : Ω), τ ω ∈ WithTop.some '' ↑s
⊢ stoppedValue u τ = ∑ i ∈ s, {ω | τ ω = ↑i}.indicator (u i)

Tactic:
ext y
  classical
  rw [stoppedValue, Finset.sum_apply, Finset.sum_indicator_eq_sum_filter]
  suffices {i ∈ s | y ∈ {ω : Ω | τ ω = (i : ι)}} = ({(τ y).untopA} : Finset ι) by
    rw [this, Finset.sum_singleton]
  ext1 ω
  simp only [Set.mem_ofPred_eq, Finset.mem_filter, Finset.mem_singleton]
  constructor <;> intro h
  · simp [h.2]
  · simp only [h]
    specialize hbdd y
    have : τ y ≠ ⊤ := fun h_contra ↦ by simp [h_contra] at hbdd
    lift τ y to ι using this with i hi
    simpa using hbdd

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

Goal state before:
pp: μ : ℝ
v : ℝ≥0
hv : v ≠ 0
s : Set ℝ
⊢ (gaussianReal μ v) s = ENNReal.ofReal (∫ (x : ℝ) in s, gaussianPDFReal μ v x)

Tactic:
by
  rw [gaussianReal_apply _ hv s, ofReal_integral_eq_lintegral_ofReal]
  · rfl
  · exact (integrable_gaussianPDFReal _ _).restrict
  · exact ae_of_all _ (gaussianPDFReal_nonneg _ _)

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

Goal state before:
pp: case refine_1
μ : ℝ
v : ℝ≥0
hv : v ≠ 0
⊢ AEMeasurable (gaussianPDF μ v) ℙ

Tactic:
exact (measurable_gaussianPDF _ _).aemeasurable

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'
⊢ ENNReal.ofReal (∫ (x : ℝ) in s', |1| * gaussianPDFReal μ v (e x)) = (gaussianReal (μ + y) v) s'

Tactic:
simp only [abs_one, one_mul]

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', gaussianPDFReal μ v (e x)) = (gaussianReal (μ + y) v) s'
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: μ : ℝ
v : ℝ≥0
z : ℂ
hv : ¬v = 0
⊢ ∫ (x : ℝ), cexp (z * ↑x) ∂gaussianReal μ v = ∫ (x : ℝ), ↑(gaussianPDFReal μ v x) * cexp (z * ↑x)

Tactic:
simp_rw [integral_gaussianReal_eq_integral_smul hv, Complex.real_smul]

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

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
v : ℝ
hv : v ∈ interior (integrableExpSet X μ)
p : ℝ
hp : 0 ≤ p
⊢ Integrable (fun ω ↦ X ω ^ p * rexp (v * X ω)) μ

Tactic:
by
  rw [mem_interior_iff_mem_nhds, mem_nhds_iff_exists_Ioo_subset] at hv
  obtain ⟨l, u, hvlu, h_subset⟩ := hv
  have h_pos : 0 < (v - l) ⊓ (u - v) := by simp [hvlu.1, hvlu.2]
  refine integrable_rpow_mul_exp_of_integrable_exp_mul
    (t := min (v - l) (u - v) / 2) ?_ ?_ ?_ hp
  · positivity
  · exact h_subset (add_half_inf_sub_mem_Ioo hvlu)
  · exact h_subset (sub_half_inf_sub_mem_Ioo hvlu)

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

Goal state before:
pp: case refine_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
⊢ MeasurableSet fun p ↦ Real.le✝ ‖p.1‖ a

Tactic:
change MeasurableSet {p : E × E | ‖p.1‖ ≤ a}
        exact measurableSet_le (by fun_prop) (by fun_prop)

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

Goal state before:
pp: a : ℝ
ha_pos : 0 < a
⊢ a / (1 - √2) < normThreshold a 0

Tactic:
simp only [normThreshold_zero]
  calc a / (1 - √2)
  _ ≤ 0 := div_nonpos_of_nonneg_of_nonpos ha_pos.le (by simp)
  _ < a := ha_pos

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

Goal state before:
pp: case neg.inr.inr
E : Type u_1
inst✝¹ : NormedAddCommGroup E
mE : MeasurableSpace E
μ : Measure E
inst✝ : NormedSpace ℝ E
p : ℝ≥0∞
c : E := ∫ (x : E), x ∂μ
h_Lp : MemLp (fun x ↦ x - c) p μ
this✝ : id = fun x ↦ x - c + c
hx : ¬c = 0
hp0 : p ≠ 0
hptop : p ≠ ∞
I : Integrable (fun x ↦ ‖x‖) μ
this : Integrable (fun x ↦ 2 ^ p.toReal * ‖x - c‖ ^ p.toReal) μ
y : E
⊢ ‖‖c‖ ^ p.toReal‖ ≤ ((fun x ↦ 2 * ‖c‖ ^ (p.toReal - 1) * ‖x‖) + fun x ↦ 2 ^ p.toReal * ‖x - c‖ ^ p.toReal) y

Tactic:
lift p to ℝ≥0 using hptop

Goal state after:
case neg.inr.inr
E : Type u_1
inst✝¹ : NormedAddCommGroup E
mE : MeasurableSpace E
μ : Measure E
inst✝ : NormedSpace ℝ E
c : E := ∫ (x : E), x ∂μ
this✝ : id = fun x ↦ x - c + c
hx : ¬c = 0
I : Integrable (fun x ↦ ‖x‖) μ
y : E
p : ℝ≥0
h_Lp : MemLp (fun x ↦ x - c) (↑p) μ
hp0 : ↑p ≠ 0
this : Integrable (fun x ↦ 2 ^ (↑p).toReal * ‖x - c‖ ^ (↑p).toReal) μ
⊢ ‖‖c‖ ^ (↑p).toReal‖ ≤
    ((fun x ↦ 2 * ‖c‖ ^ ((↑p).toReal - 1) * ‖x‖) + fun x ↦ 2 ^ (↑p).toReal * ‖x - c‖ ^ (↑p).toReal) y
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_gt : 2⁻¹ < μ {x | ‖x‖ ≤ a}
n : ℕ
c : ℝ≥0∞ := μ {x | ‖x‖ ≤ a}
⊢ μ {x | normThreshold a n < ‖x‖} ≤ μ {x | ‖x‖ ≤ a} * ((1 - μ {x | ‖x‖ ≤ a}) / μ {x | ‖x‖ ≤ a}) ^ 2 ^ n

Tactic:
replace hc_gt : 2⁻¹ < c := ha_gt

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_gt : 2⁻¹ < μ {x | ‖x‖ ≤ a}
n : ℕ
c : ℝ≥0∞ := μ {x | ‖x‖ ≤ a}
hc_gt : 2⁻¹ < c
⊢ μ {x | normThreshold a n < ‖x‖} ≤ μ {x | ‖x‖ ≤ a} * ((1 - μ {x | ‖x‖ ≤ a}) / μ {x | ‖x‖ ≤ a}) ^ 2 ^ n
Module: Mathlib.Probability.Distributions.Fernique

Goal state before:
pp: case e_a
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}
ha_lt : μ {x | ‖x‖ ≤ a} < 1
n : ℕ
c : ℝ≥0∞ := μ {x | ‖x‖ ≤ a}
hc_pos : 0 < c
hc_lt : c < 1
hc_lt_top : c < ∞
hc_one_sub_lt_top : 1 - c < ∞
hc_ratio_pos : 0 < (c / (1 - c)).toReal
⊢ ((1 - μ {x | ‖x‖ ≤ a}) / μ {x | ‖x‖ ≤ a}) ^ 2 ^ n =
    ENNReal.ofReal (rexp (Real.log (μ {x | ‖x‖ ≤ a} / (1 - μ {x | ‖x‖ ≤ a})).toReal⁻¹ * 2 ^ n))

Tactic:
mul_comm (Real.log _),

Goal state after:
case e_a
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}
ha_lt : μ {x | ‖x‖ ≤ a} < 1
n : ℕ
c : ℝ≥0∞ := μ {x | ‖x‖ ≤ a}
hc_pos : 0 < c
hc_lt : c < 1
hc_lt_top : c < ∞
hc_one_sub_lt_top : 1 - c < ∞
hc_ratio_pos : 0 < (c / (1 - c)).toReal
⊢ ((1 - μ {x | ‖x‖ ≤ a}) / μ {x | ‖x‖ ≤ a}) ^ 2 ^ n =
    ENNReal.ofReal (rexp (2 ^ n * Real.log (μ {x | ‖x‖ ≤ a} / (1 - μ {x | ‖x‖ ≤ a})).toReal⁻¹))
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_gt : 2⁻¹ < μ {x | ‖x‖ ≤ a}
ha_lt : μ {x | ‖x‖ ≤ a} < 1
n : ℕ
⊢ μ {x | normThreshold a n < ‖x‖} ≤
    μ {x | ‖x‖ ≤ a} * ENNReal.ofReal (rexp (-Real.log (μ {x | ‖x‖ ≤ a} / (1 - μ {x | ‖x‖ ≤ a})).toReal * 2 ^ n))

Tactic:
by
  let c := μ {x | ‖x‖ ≤ a}
  have hc_pos : 0 < c := lt_of_lt_of_le (by simp) ha_gt.le
  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 hc_ratio_pos : 0 < (c / (1 - c)).toReal := by
    rw [ENNReal.toReal_div, div_pos_iff_of_pos_right]
    · simp [ENNReal.toReal_pos_iff, hc_pos, hc_lt_top]
    · simp [ENNReal.toReal_pos_iff, tsub_pos_iff_lt, hc_lt, hc_one_sub_lt_top]
  refine (measure_gt_normThreshold_le_rpow h_rot ha_gt n).trans_eq ?_
  congr
  rw [← Real.log_inv, mul_comm (Real.log _), ← Real.log_rpow (by positivity),
    Real.exp_log (by positivity), ← ENNReal.ofReal_rpow_of_nonneg (by positivity) (by positivity),
    ENNReal.toReal_div, inv_div, ← ENNReal.toReal_div, ENNReal.ofReal_toReal]
  · norm_cast
  · exact ENNReal.div_ne_top (by finiteness) (lt_trans (by simp) ha_gt).ne'

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'))
⊢ Set.univ =ᵐ[μ] closedBall 0 a

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
ha : μ {x | ‖x‖ ≤ a} = 1
ht_int_zero : ∫⁻ (x : E) in closedBall 0 a, ENNReal.ofReal (rexp (C * ‖x‖ ^ 2)) ∂μ ≤ ENNReal.ofReal (rexp (logRatio c'))
⊢ Measurable fun x ↦ ‖x‖ ≤ 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:
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 : μ {x | ‖x‖ ≤ a} = 1
ht_int_zero : ∫⁻ (x : E) in closedBall 0 a, ENNReal.ofReal (rexp (C * ‖x‖ ^ 2)) ∂μ ≤ ENNReal.ofReal (rexp (logRatio c'))
⊢ Measurable fun x ↦ ‖x‖ ≤ a
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
h_meas_Ioo : ∀ (a : ℝ), 0 < a → 2⁻¹ < μ {x | ‖x‖ ≤ a} → 1 ≤ μ {x | ‖x‖ ≤ a}
h_ne : ∀ (b : ℝ), μ {x | ‖x‖ ≤ b} ≠ 1
h_le : ∀ (a : ℝ), μ {x | ‖x‖ ≤ a} ≤ 2⁻¹
h_univ : Set.univ = ⋃ a, {x | ‖x‖ ≤ ↑a}
a b : ℕ
⊢ a ≤ b → (fun a ↦ {x | ‖x‖ ≤ ↑a}) a ⊆ (fun a ↦ {x | ‖x‖ ≤ ↑a}) b

Tactic:
hab

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✝ : IsProbabilityMeasure μ
h_rot : Measure.map (⇑(ContinuousLinearMap.rotation (-(π / 4)))) (μ.prod μ) = μ.prod μ
h_meas_Ioo : ∀ (a : ℝ), 0 < a → 2⁻¹ < μ {x | ‖x‖ ≤ a} → 1 ≤ μ {x | ‖x‖ ≤ a}
h_ne : ∀ (b : ℝ), μ {x | ‖x‖ ≤ b} ≠ 1
h_le : ∀ (a : ℝ), μ {x | ‖x‖ ≤ a} ≤ 2⁻¹
h_univ : Set.univ = ⋃ a, {x | ‖x‖ ≤ ↑a}
a b : ℕ
hab : a ≤ b
⊢ (fun a ↦ {x | ‖x‖ ≤ ↑a}) a ⊆ (fun a ↦ {x | ‖x‖ ≤ ↑a}) b
Module: Mathlib.Probability.Independence.Process.HasIndepIncrements.IsGaussianProcess

Goal state before:
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.orderEmbOfFin ⋯) (i.pred hi) ≤ I.orderEmbOfFinWithBot j

Tactic:
orderEmbOfFinWithBot_of_ne_zero I j (by grind)

Goal state after:
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.orderEmbOfFin ⋯) (i.pred hi) ≤ (I.orderEmbOfFin ⋯) (j.pred ⋯)