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

Goal state before:
pp: case refine_3.inr
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
⊢ (if hb : ∃ b, P.Rel ↑a ↑b then f₀ hb.choose else P.rep ⋯) = f₀ a

Tactic:
split_ifs with h

Goal state after:
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

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.Types.Defs

Goal state before:
pp: α β : Type u
inst✝¹ : LinearOrder α
inst✝ : LinearOrder β
h : lift.{u_1, u} (type α) ≤ lift.{u_1, u} (type β)
⊢ Nonempty (α ↪o β)

Tactic:
rw [← type_ulift, ← type_ulift, type_le_type_iff] at h

Goal state after:
α β : Type u
inst✝¹ : LinearOrder α
inst✝ : LinearOrder β
h : Nonempty (ULift α ↪o ULift β)
⊢ Nonempty (α ↪o β)
Module: Mathlib.Probability.Process.HittingTime

Goal state before:
pp: Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝ : ConditionallyCompleteLinearOrder ι
u : ι → Ω → β
s : Set β
n m : ι
ω : Ω
⊢ (if ∃ j ∈ Set.Icc n m, u j ω ∈ s then sInf (Set.Icc n m ∩ {i | u i ω ∈ s}) else m) ≤ m

Tactic:
split_ifs with h

Goal state after:
case pos
Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝ : ConditionallyCompleteLinearOrder ι
u : ι → Ω → β
s : Set β
n m : ι
ω : Ω
h : ∃ j ∈ Set.Icc n m, u j ω ∈ s
⊢ sInf (Set.Icc n m ∩ {i | u i ω ∈ s}) ≤ m

case neg
Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝ : ConditionallyCompleteLinearOrder ι
u : ι → Ω → β
s : Set β
n m : ι
ω : Ω
h : ¬∃ j ∈ Set.Icc n m, u j ω ∈ s
⊢ m ≤ m
Module: Mathlib.Probability.Process.HittingTime

Goal state before:
pp: Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝ : ConditionallyCompleteLinearOrder ι
u : ι → Ω → β
s : Set β
n m : ι
hnm : n ≤ m
ω : Ω
⊢ n ≤ if ∃ j ∈ Set.Icc n m, u j ω ∈ s then sInf (Set.Icc n m ∩ {i | u i ω ∈ s}) else m

Tactic:
split_ifs with h

Goal state after:
case pos
Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝ : ConditionallyCompleteLinearOrder ι
u : ι → Ω → β
s : Set β
n m : ι
hnm : n ≤ m
ω : Ω
h : ∃ j ∈ Set.Icc n m, u j ω ∈ s
⊢ n ≤ sInf (Set.Icc n m ∩ {i | u i ω ∈ s})

case neg
Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝ : ConditionallyCompleteLinearOrder ι
u : ι → Ω → β
s : Set β
n m : ι
hnm : n ≤ m
ω : Ω
h : ¬∃ j ∈ Set.Icc n m, u j ω ∈ s
⊢ n ≤ m
Module: Mathlib.Probability.Process.HittingTime

Goal state before:
pp: Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝ : ConditionallyCompleteLinearOrder ι
u : ι → Ω → β
s : Set β
n : ι
ω : Ω
⊢ ↑n ≤ if ∃ j, n ≤ j ∧ u j ω ∈ s then ↑(sInf {i | n ≤ i ∧ u i ω ∈ s}) else ⊤

Tactic:
split_ifs with h

Goal state after:
case pos
Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝ : ConditionallyCompleteLinearOrder ι
u : ι → Ω → β
s : Set β
n : ι
ω : Ω
h : ∃ j, n ≤ j ∧ u j ω ∈ s
⊢ ↑n ≤ ↑(sInf {j | n ≤ j ∧ u j ω ∈ s})

case neg
Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝ : ConditionallyCompleteLinearOrder ι
u : ι → Ω → β
s : Set β
n : ι
ω : Ω
h : ¬∃ j, n ≤ j ∧ u j ω ∈ s
⊢ ↑n ≤ ⊤
Module: Mathlib.Probability.Process.Stopping

Goal state before:
pp: case refine_1
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
h_lub : IsLUB (Set.Iio i) i
hi_min : ¬IsMin i
seq : ℕ → ι
h_tendsto : Tendsto seq atTop (𝓝 i)
h_bound : ∀ (j : ℕ), seq j < i
k : WithTop ι
hk_lt_i : k < ↑i
⊢ ∃ i, k ≤ ↑(seq i)

Tactic:
rw [tendsto_atTop'] at h_tendsto

Goal state after:
case refine_1
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
h_lub : IsLUB (Set.Iio i) i
hi_min : ¬IsMin i
seq : ℕ → ι
h_tendsto : ∀ s ∈ 𝓝 i, ∃ a, ∀ (b : ℕ), a ≤ b → seq b ∈ s
h_bound : ∀ (j : ℕ), seq j < i
k : WithTop ι
hk_lt_i : k < ↑i
⊢ ∃ i, k ≤ ↑(seq i)
Module: Mathlib.Probability.Process.HittingTime

Goal state before:
pp: Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝ : ConditionallyCompleteLinearOrder ι
u : ι → Ω → β
s : Set β
⊢ Monotone (hittingAfter u s)

Tactic:
intro n m hnm ω
  simp only [hittingAfter]
  split_ifs with h_n h_m h_m
  · norm_cast
    gcongr
    exacts [⟨n, by simp [mem_lowerBounds]; grind⟩]
  · simp
  · have ⟨t, ht⟩ := h_m
    exact absurd ⟨t, hnm.trans ht.1, ht.2⟩ h_n
  · simp

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

Goal state before:
pp: case pos
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) ≤ ∑ k ∈ Finset.range N, upcrossingStrat a b f N k ω * (f (k + 1) - f k) ω

Tactic:
simp [hN]

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

Goal state before:
pp: 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
⊢ sᶜ ∩ {ω | τ ω ≤ ↑i} = (sᶜ ∪ {ω | τ ω ≤ ↑i}ᶜ) ∩ {ω | τ ω ≤ ↑i}

Tactic:
rw [Set.union_inter_distrib_right]

Goal state after:
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
⊢ sᶜ ∩ {ω | τ ω ≤ ↑i} = sᶜ ∩ {ω | τ ω ≤ ↑i} ∪ {ω | τ ω ≤ ↑i}ᶜ ∩ {ω | τ ω ≤ ↑i}
Module: Mathlib.Probability.Process.Stopping

Goal state before:
pp: 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
i : ι
⊢ MeasurableSet {ω | ↑i < τ ω}

Tactic:
have : {ω : Ω | i < τ ω} = {ω : Ω | τ ω ≤ i}ᶜ := by ext1 ω; simp

Goal state after:
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
i : ι
this : {ω | ↑i < τ ω} = {ω | τ ω ≤ ↑i}ᶜ
⊢ MeasurableSet {ω | ↑i < τ ω}
Module: Mathlib.Probability.Martingale.Upcrossing

Goal state before:
pp: case pos
Ω : Type u_1
a b : ℝ
f : ℕ → Ω → ℝ
N : ℕ
ω : Ω
hab : a < b
hN : N = 0
⊢ upcrossingsBefore a b f N ω = ∑ i ∈ Finset.Ico 1 (N + 1), {n | upperCrossingTime a b f N n ω < N}.indicator 1 i

Tactic:
simp [hN]

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

Goal state before:
pp: Ω : Type u_1
m0 : MeasurableSpace Ω
a b : ℝ
f : ℕ → Ω → ℝ
N : ℕ
ℱ : Filtration ℕ m0
hf : StronglyAdapted ℱ f
hab : a < b
this :
  upcrossingsBefore a b f N = fun ω ↦ ∑ i ∈ Finset.Ico 1 (N + 1), {n | upperCrossingTime a b f N n ω < N}.indicator 1 i
⊢ Measurable fun ω ↦ ∑ i ∈ Finset.Ico 1 (N + 1), {n | upperCrossingTime a b f N n ω < N}.indicator 1 i

Tactic:
refine Finset.measurable_fun_sum _ fun i _ => Measurable.indicator measurable_const <|
    ℱ.le N _ ?_

Goal state after:
Ω : Type u_1
m0 : MeasurableSpace Ω
a b : ℝ
f : ℕ → Ω → ℝ
N : ℕ
ℱ : Filtration ℕ m0
hf : StronglyAdapted ℱ f
hab : a < b
this :
  upcrossingsBefore a b f N = fun ω ↦ ∑ i ∈ Finset.Ico 1 (N + 1), {n | upperCrossingTime a b f N n ω < N}.indicator 1 i
i : ℕ
x✝ : i ∈ Finset.Ico 1 (N + 1)
⊢ MeasurableSet fun ω ↦ {n | upperCrossingTime a b f N n ω < N} i
Module: Mathlib.Probability.Martingale.BorelCantelli

Goal state before:
pp: case mpr
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 : Martingale f ℱ μ
hbdd : ∀ᵐ (ω : Ω) ∂μ, ∀ (i : ℕ), |f (i + 1) ω - f i ω| ≤ ↑R
hbdd' : ∀ᵐ (ω : Ω) ∂μ, ∀ (i : ℕ), |(-f) (i + 1) ω - (-f) i ω| ≤ ↑R
hup : ∀ᵐ (ω : Ω) ∂μ, BddAbove (Set.range fun n ↦ f n ω) ↔ ∃ c, Tendsto (fun n ↦ f n ω) atTop (𝓝 c)
hdown : ∀ᵐ (ω : Ω) ∂μ, BddAbove (Set.range fun n ↦ (-f) n ω) ↔ ∃ c, Tendsto (fun n ↦ (-f) n ω) atTop (𝓝 c)
ω✝ : Ω
hω₁ : BddAbove (Set.range fun n ↦ f n ω✝) ↔ ∃ c, Tendsto (fun n ↦ f n ω✝) atTop (𝓝 c)
hω₂ : BddAbove (Set.range fun n ↦ (-f) n ω✝) ↔ ∃ c, Tendsto (fun n ↦ (-f) n ω✝) atTop (𝓝 c)
this : (∃ c, Tendsto (fun n ↦ f n ω✝) atTop (𝓝 c)) ↔ ∃ c, Tendsto (fun n ↦ (-f) n ω✝) atTop (𝓝 c)
c : ℝ
hc : c ∈ lowerBounds (Set.range fun n ↦ f n ω✝)
ω : ℝ
hω : ω ∈ Set.range fun n ↦ (-f) n ω✝
⊢ ω ≤ -c

Tactic:
rw [mem_lowerBounds] at hc

Goal state after:
case mpr
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 : Martingale f ℱ μ
hbdd : ∀ᵐ (ω : Ω) ∂μ, ∀ (i : ℕ), |f (i + 1) ω - f i ω| ≤ ↑R
hbdd' : ∀ᵐ (ω : Ω) ∂μ, ∀ (i : ℕ), |(-f) (i + 1) ω - (-f) i ω| ≤ ↑R
hup : ∀ᵐ (ω : Ω) ∂μ, BddAbove (Set.range fun n ↦ f n ω) ↔ ∃ c, Tendsto (fun n ↦ f n ω) atTop (𝓝 c)
hdown : ∀ᵐ (ω : Ω) ∂μ, BddAbove (Set.range fun n ↦ (-f) n ω) ↔ ∃ c, Tendsto (fun n ↦ (-f) n ω) atTop (𝓝 c)
ω✝ : Ω
hω₁ : BddAbove (Set.range fun n ↦ f n ω✝) ↔ ∃ c, Tendsto (fun n ↦ f n ω✝) atTop (𝓝 c)
hω₂ : BddAbove (Set.range fun n ↦ (-f) n ω✝) ↔ ∃ c, Tendsto (fun n ↦ (-f) n ω✝) atTop (𝓝 c)
this : (∃ c, Tendsto (fun n ↦ f n ω✝) atTop (𝓝 c)) ↔ ∃ c, Tendsto (fun n ↦ (-f) n ω✝) atTop (𝓝 c)
c : ℝ
hc : ∀ x ∈ Set.range fun n ↦ f n ω✝, c ≤ x
ω : ℝ
hω : ω ∈ Set.range fun n ↦ (-f) n ω✝
⊢ ω ≤ -c
Module: Mathlib.Probability.Process.Stopping

Goal state before:
pp: Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝⁴ : LinearOrder ι
μ : Measure Ω
ℱ : Filtration ι m
τ : Ω → WithTop ι
E : Type u_4
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
inst✝¹ : CompleteSpace E
f : Ω → E
hτ : IsStoppingTime ℱ τ
i : ι
inst✝ : SigmaFinite (μ.trim ⋯)
⊢ ⋯.measurableSpace ≤ hτ.measurableSpace

Tactic:
rw [IsStoppingTime.measurableSpace_min_const]
      exact inf_le_left

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

Goal state before:
pp: case refine_2
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t v x : ℝ
h_int_pos : Integrable (fun ω ↦ rexp ((v + t) * X ω)) μ
h_int_neg : Integrable (fun ω ↦ rexp ((v - t) * X ω)) μ
h_nonneg : 0 ≤ x
hx : x < |t|
p : ℝ
hp : 0 ≤ p
ht : t ≠ 0
hX : AEMeasurable X μ
⊢ Integrable (fun a ↦ ‖|X a| ^ p * rexp (v * X a + x * |X a|)‖) μ

Tactic:
simp only [norm_mul, norm_eq_abs, abs_exp]

Goal state after:
case refine_2
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t v x : ℝ
h_int_pos : Integrable (fun ω ↦ rexp ((v + t) * X ω)) μ
h_int_neg : Integrable (fun ω ↦ rexp ((v - t) * X ω)) μ
h_nonneg : 0 ≤ x
hx : x < |t|
p : ℝ
hp : 0 ≤ p
ht : t ≠ 0
hX : AEMeasurable X μ
⊢ Integrable (fun a ↦ ||X a| ^ p| * rexp (v * X a + x * |X a|)) μ
Module: Mathlib.Probability.Moments.IntegrableExpMul

Goal state before:
pp: case refine_2.refine_3
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t v x : ℝ
h_int_pos : Integrable (fun ω ↦ rexp ((v + t) * X ω)) μ
h_int_neg : Integrable (fun ω ↦ rexp ((v - t) * X ω)) μ
h_nonneg : 0 ≤ x
hx : x < |t|
p : ℝ
hp : 0 ≤ p
ht : t ≠ 0
hX : AEMeasurable X μ
h_le : ∀ (a : Ω), |X a| ^ p * rexp (v * X a + x * |X a|) ≤ (p / (|t| - x)) ^ p * rexp (v * X a + |t| * |X a|)
ω : Ω
⊢ ‖||X ω| ^ p| * rexp (v * X ω + x * |X ω|)‖ ≤ ‖(p / (|t| - x)) ^ p * rexp (v * X ω + |t| * |X ω|)‖

Tactic:
simp only [norm_mul, norm_eq_abs, abs_exp]

Goal state after:
case refine_2.refine_3
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t v x : ℝ
h_int_pos : Integrable (fun ω ↦ rexp ((v + t) * X ω)) μ
h_int_neg : Integrable (fun ω ↦ rexp ((v - t) * X ω)) μ
h_nonneg : 0 ≤ x
hx : x < |t|
p : ℝ
hp : 0 ≤ p
ht : t ≠ 0
hX : AEMeasurable X μ
h_le : ∀ (a : Ω), |X a| ^ p * rexp (v * X a + x * |X a|) ≤ (p / (|t| - x)) ^ p * rexp (v * X a + |t| * |X a|)
ω : Ω
⊢ |(||X ω| ^ p|)| * rexp (v * X ω + x * |X ω|) ≤ |(p / (|t| - x)) ^ p| * rexp (v * X ω + |t| * |X ω|)
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}
hc_gt : 2⁻¹ < c
hc_pos : 0 < c
⊢ μ {x | normThreshold a n < ‖x‖} ≤ μ {x | ‖x‖ ≤ a} * ((1 - μ {x | ‖x‖ ≤ a}) / μ {x | ‖x‖ ≤ a}) ^ 2 ^ n

Tactic:
have hc_lt_top : c < ∞ := measure_lt_top _ _

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
hc_pos : 0 < c
hc_lt_top : 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: 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
⊢ 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:
have hc_lt_top : c < ∞ := measure_lt_top _ _

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}
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 < ∞
⊢ 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))
Module: Mathlib.Probability.Moments.CovarianceBilinDual

Goal state before:
pp: 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
h✝ : ‖c‖ / 2 ≤ ‖y‖
⊢ 2 * ‖c‖ ^ (↑p - 1) * ‖y‖ + 0 ≤ 2 * ‖c‖ ^ (↑p - 1) * ‖y‖ + 2 ^ ↑p * ‖y - c‖ ^ ↑p

Tactic:
by gcongr; positivity

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

Goal state before:
pp: case refine_1
c : ℝ≥0∞
hc_gt : 2⁻¹ < c
hc_lt : c < 1
⊢ 0 < (1 - c).toReal

Tactic:
simp only [ENNReal.toReal_pos_iff, tsub_pos_iff_lt, hc_lt, true_and]

Goal state after:
case refine_1
c : ℝ≥0∞
hc_gt : 2⁻¹ < c
hc_lt : c < 1
⊢ 1 - c < ∞
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
⊢ 0 < (1 - c).toReal

Tactic:
simp only [ENNReal.toReal_pos_iff, tsub_pos_iff_lt, hc_lt, true_and]

Goal state after:
case pos
a : ℝ
c : ℝ≥0∞
hc_gt : 2⁻¹ < c
hc_lt : c < 1
n : ℕ
ha : a = 0
⊢ 1 - c < ∞
Module: Mathlib.Probability.Distributions.Gaussian.Multivariate

Goal state before:
pp: ι : Type u_1
inst✝¹ : Fintype ι
inst✝ : DecidableEq ι
μ : EuclideanSpace ℝ ι
S : Matrix ι ι ℝ
hS : S.PosSemidef
i : ι
⊢ Var[fun x ↦ x.ofLp i; multivariateGaussian μ S] = S i i

Tactic:
← covariance_self,

Goal state after:
ι : Type u_1
inst✝¹ : Fintype ι
inst✝ : DecidableEq ι
μ : EuclideanSpace ℝ ι
S : Matrix ι ι ℝ
hS : S.PosSemidef
i : ι
⊢ cov[fun x ↦ x.ofLp i, fun x ↦ x.ofLp i; multivariateGaussian μ S] = S i i

ι : Type u_1
inst✝¹ : Fintype ι
inst✝ : DecidableEq ι
μ : EuclideanSpace ℝ ι
S : Matrix ι ι ℝ
hS : S.PosSemidef
i : ι
⊢ AEMeasurable (fun x ↦ x.ofLp i) (multivariateGaussian μ S)
Module: Mathlib.Probability.BrownianMotion.GaussianProjectiveFamily

Goal state before:
pp: I : Finset ℝ≥0
s : ↥I
⊢ Var[fun x ↦ x s; projectiveFamily I] = ↑↑s

Tactic:
← covariance_self,

Goal state after:
I : Finset ℝ≥0
s : ↥I
⊢ cov[fun x ↦ x s, fun x ↦ x s; projectiveFamily I] = ↑↑s

I : Finset ℝ≥0
s : ↥I
⊢ AEMeasurable (fun x ↦ x s) (projectiveFamily I)
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 : 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'))
⊢ ∫⁻ (x : E) in Set.univ, ENNReal.ofReal (rexp (C * ‖x‖ ^ 2)) ∂μ =
    ∫⁻ (x : E) in closedBall 0 a, ENNReal.ofReal (rexp (C * ‖x‖ ^ 2)) ∂μ

Tactic:
refine setLIntegral_congr ?_

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 : 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'))
⊢ Set.univ =ᵐ[μ] closedBall 0 a
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'))
⊢ ∀ᵐ (a_1 : E) ∂μ, a_1 ∈ closedBall 0 a

Tactic:
filter_upwards [ha] with x hx using by simp [hx]

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 : ℝ≥0∞ := μ {x | ‖x‖ ≤ a}
hc_lt : c < 1
⊢ ∃ C, 0 < C ∧ Integrable (fun x ↦ rexp (C * ‖x‖ ^ 2)) μ

Tactic:
have hc_lt_top : c < ∞ := measure_lt_top _ _

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
a : ℝ
ha_pos : 0 < a
ha_gt : 2⁻¹ < μ {x | ‖x‖ ≤ a}
ha_lt : μ {x | ‖x‖ ≤ a} < 1
c : ℝ≥0∞ := μ {x | ‖x‖ ≤ a}
hc_lt : c < 1
hc_lt_top : c < ∞
⊢ ∃ C, 0 < C ∧ Integrable (fun x ↦ rexp (C * ‖x‖ ^ 2)) μ
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
𝓧 : S → Type u_5
𝓨 : Type u_6
inst✝² : (i : S) → MeasurableSpace (𝓧 i)
inst✝¹ : MeasurableSpace 𝓨
X : (i : S) → Ω → 𝓧 i
Y : Ω → 𝓨
hX : ∀ (i : S), Measurable (X i)
hY : Measurable Y
h : ∀ (I : Finset S), IndepFun (fun ω i ↦ X (↑i) ω) Y κ P
inst✝ : IsZeroOrMarkovKernel κ
πX : Set (Set Ω) := {s | ∃ t ∈ squareCylinders fun i ↦ {s | MeasurableSet s}, (fun ω i ↦ X i ω) ⁻¹' t = s}
πX_pi : IsPiSystem πX
πX_gen : MeasurableSpace.comap (fun ω i ↦ X i ω) MeasurableSpace.pi = generateFrom πX
I : Finset S
s : (i : S) → Set (𝓧 i)
t : Set 𝓨
ht : MeasurableSet t
hs : ∀ (i : S), MeasurableSet (s i)
this : (fun ω i ↦ X i ω) ⁻¹' (↑I).pi s = (fun ω i ↦ X (↑i) ω) ⁻¹' (↑Finset.univ).pi fun i ↦ s ↑i
h1 : MeasurableSet ((↑Finset.univ).pi fun i ↦ s ↑i)
ω : α
hω :
  (κ ω) (((fun ω i ↦ X (↑i) ω) ⁻¹' (↑Finset.univ).pi fun i ↦ s ↑i) ∩ Y ⁻¹' t) =
    (κ ω) ((fun ω i ↦ X (↑i) ω) ⁻¹' (↑Finset.univ).pi fun i ↦ s ↑i) * (κ ω) (Y ⁻¹' t)
⊢ (κ ω) ((fun ω i ↦ X i ω) ⁻¹' (↑I).pi s ∩ Y ⁻¹' t) = (κ ω) ((fun ω i ↦ X i ω) ⁻¹' (↑I).pi s) * (κ ω) (Y ⁻¹' t)

Tactic:
rw [this, hω]

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 : Type u_5
𝓧 : S → Type u_6
𝓨 : T → Type u_7
inst✝² : (i : S) → MeasurableSpace (𝓧 i)
inst✝¹ : (j : T) → MeasurableSpace (𝓨 j)
X : (i : S) → Ω → 𝓧 i
Y : (j : T) → Ω → 𝓨 j
hX : ∀ (i : S), Measurable (X i)
hY : ∀ (j : T), Measurable (Y j)
h : ∀ (I : Finset S) (J : Finset T), IndepFun (fun ω i ↦ X (↑i) ω) (fun ω j ↦ Y (↑j) ω) κ P
inst✝ : IsZeroOrMarkovKernel κ
⊢ IndepFun (fun ω i ↦ X i ω) (fun ω j ↦ Y j ω) κ P

Tactic:
refine IndepFun.process_indepFun hX (measurable_pi_lambda _ hY) fun I ↦ ?_
  exact IndepFun.indepFun_process (measurable_pi_lambda _ fun _ ↦ hX _) hY fun J ↦ h I J

Goal state after:
Module: Mathlib.Probability.Independence.Process.HasIndepIncrements.IsGaussianProcess

Goal state before:
pp: T : Type u_1
inst✝¹ : LinearOrder T
I : Finset T
inst✝ : Bot T
i : Fin #I
⊢ I.orderEmbOfFinWithBot i.succ = (I.orderEmbOfFin ⋯) i

Tactic:
orderEmbOfFinWithBot_of_ne_zero,

Goal state after:
T : Type u_1
inst✝¹ : LinearOrder T
I : Finset T
inst✝ : Bot T
i : Fin #I
⊢ (I.orderEmbOfFin ⋯) (i.succ.pred ?hi) = (I.orderEmbOfFin ⋯) i

case hi
T : Type u_1
inst✝¹ : LinearOrder T
I : Finset T
inst✝ : Bot T
i : Fin #I
⊢ i.succ ≠ 0
Module: Mathlib.Probability.Kernel.Disintegration.MeasurableStieltjes

Goal state before:
pp: α : Type u_1
f g : α → ℚ → ℝ
a : α
p : α → Prop
inst✝ : DecidablePred p
hf : p a → IsRatStieltjesPoint f a
hg : ¬p a → IsRatStieltjesPoint g a
⊢ Monotone (if p a then f a else g a)

Tactic:
split_ifs with h

Goal state after:
case pos
α : Type u_1
f g : α → ℚ → ℝ
a : α
p : α → Prop
inst✝ : DecidablePred p
hf : p a → IsRatStieltjesPoint f a
hg : ¬p a → IsRatStieltjesPoint g a
h : p a
⊢ Monotone (f a)

case neg
α : Type u_1
f g : α → ℚ → ℝ
a : α
p : α → Prop
inst✝ : DecidablePred p
hf : p a → IsRatStieltjesPoint f a
hg : ¬p a → IsRatStieltjesPoint g a
h : ¬p a
⊢ Monotone (g a)