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

Goal state before:
pp: case succ
Ω : Type u_1
a b : ℝ
f : ℕ → Ω → ℝ
N n : ℕ
ω : Ω
M : ℕ
hNM : N ≤ M
k : ℕ
ih :
  lowerCrossingTime a b f N k ω < N →
    upperCrossingTime a b f N k ω < N →
      upperCrossingTime a b f M k ω = upperCrossingTime a b f N k ω ∧
        lowerCrossingTime a b f M k ω = lowerCrossingTime a b f N k ω
h : lowerCrossingTime a b f N (k + 1) ω < N
h' : upperCrossingTime a b f N (k + 1) ω < N
⊢ upperCrossingTime a b f M (k + 1) ω = upperCrossingTime a b f N (k + 1) ω ∧
    lowerCrossingTime a b f M (k + 1) ω = lowerCrossingTime a b f N (k + 1) ω

Tactic:
specialize ih (lt_of_le_of_lt (lowerCrossingTime_mono (Nat.le_succ _)) h)
      (lt_of_le_of_lt (upperCrossingTime_mono (Nat.le_succ _)) h')
    have : upperCrossingTime a b f M k.succ ω = upperCrossingTime a b f N k.succ ω := by
      rw [upperCrossingTime_succ_eq, hittingBtwn_lt_iff] at h'
      · simp only [upperCrossingTime_succ_eq]
        obtain ⟨j, hj₁, hj₂⟩ := h'
        rw [eq_comm, ih.2]
        exact hittingBtwn_eq_hittingBtwn_of_exists hNM ⟨j, ⟨hj₁.1, hj₁.2.le⟩, hj₂⟩
      · exact le_rfl
    refine ⟨this, ?_⟩
    simp only [lowerCrossingTime, eq_comm, this, Nat.succ_eq_add_one]
    refine hittingBtwn_eq_hittingBtwn_of_exists hNM ?_
    rw [lowerCrossingTime, hittingBtwn_lt_iff _ le_rfl] at h
    obtain ⟨j, hj₁, hj₂⟩ := h
    exact ⟨j, ⟨hj₁.1, hj₁.2.le⟩, hj₂⟩

Goal state after:
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
m n n' : ι
hnn' : n ≤ n'
ω : Ω
⊢ (fun x ↦ hittingBtwn u s x m) n ω ≤ (fun x ↦ hittingBtwn u s x m) n' ω

Tactic:
simp only [hittingBtwn]

Goal state after:
Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝ : ConditionallyCompleteLinearOrder ι
u : ι → Ω → β
s : Set β
m n n' : ι
hnn' : n ≤ n'
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) ≤
    if ∃ j ∈ Set.Icc n' m, u j ω ∈ s then sInf (Set.Icc n' m ∩ {i | u i ω ∈ s}) else m
Module: Mathlib.Probability.Martingale.Upcrossing

Goal state before:
pp: case refine_2
Ω : Type u_1
a b : ℝ
f : ℕ → Ω → ℝ
N n : ℕ
hab : a < b
hab' : 0 < b - a
i : Ω
ω : ℕ
h : b ≤ f ω i
⊢ b - a ≤ (f ω i - a)⁺

Tactic:
rw [← sub_le_sub_iff_right a] at h

Goal state after:
case refine_2
Ω : Type u_1
a b : ℝ
f : ℕ → Ω → ℝ
N n : ℕ
hab : a < b
hab' : 0 < b - a
i : Ω
ω : ℕ
h : b - a ≤ f ω i - a
⊢ b - a ≤ (f ω i - a)⁺
Module: Mathlib.Probability.Process.Stopping

Goal state before:
pp: case pos
Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝ : Preorder ι
f : Filtration ι m
i : ι
s : Set Ω
h : MeasurableSet s
j : ι
hij : i ≤ j
⊢ MeasurableSet (s ∩ {ω | ↑i ≤ ↑j})

Tactic:
norm_cast
      simp only [hij, Set.ofPred_true, Set.inter_univ]
      exact f.mono hij _ h

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

Goal state before:
pp: case pos
Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝ : Preorder ι
f : Filtration ι m
τ : Ω → WithTop ι
hτ : IsStoppingTime f τ
s : Set Ω
i : ι
this : ∀ (j : WithTop ι), {ω | τ ω = ↑i} ∩ {ω | τ ω ≤ j} = {ω | τ ω = ↑i} ∩ {_ω | ↑i ≤ j}
h : MeasurableSet (s ∩ {ω | τ ω = ↑i})
j : ι
hij : i ≤ j
⊢ MeasurableSet (s ∩ ({ω | τ ω = ↑i} ∩ {_ω | ↑i ≤ ↑j}))

Tactic:
norm_cast
      simp only [hij, Set.ofPred_true, Set.inter_univ]
      exact f.mono hij _ h

Goal state after:
Module: Mathlib.Probability.ConditionalExpectation

Goal state before:
pp: case pos.refine_3
Ω : Type u_1
E : Type u_2
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
inst✝¹ : CompleteSpace E
m₁ m₂ m : MeasurableSpace Ω
μ : Measure Ω
f : Ω → E
hle₁ : m₁ ≤ m
hle₂ : m₂ ≤ m
inst✝ : SigmaFinite (μ.trim hle₂)
hf : StronglyMeasurable f
hindp : Indep m₁ m₂ μ
hfint : MemLp f 1 μ
s : Set Ω
hms : MeasurableSet s
hs : μ s < ∞
⊢ ∀ ⦃f g : Ω → E⦄,
    Disjoint (Function.support f) (Function.support g) →
      MemLp f 1 μ →
        MemLp g 1 μ →
          StronglyMeasurable f →
            StronglyMeasurable g →
              μ.real s • ∫ (x : Ω), f x ∂μ = ∫ (x : Ω) in s, f x ∂μ →
                μ.real s • ∫ (x : Ω), g x ∂μ = ∫ (x : Ω) in s, g x ∂μ →
                  μ.real s • ∫ (x : Ω), (f + g) x ∂μ = ∫ (x : Ω) in s, (f + g) x ∂μ

Tactic:
intro u v _ huint hvint hu hv hu_eq hv_eq

Goal state after:
case pos.refine_3
Ω : Type u_1
E : Type u_2
inst✝³ : NormedAddCommGroup E
inst✝² : NormedSpace ℝ E
inst✝¹ : CompleteSpace E
m₁ m₂ m : MeasurableSpace Ω
μ : Measure Ω
f : Ω → E
hle₁ : m₁ ≤ m
hle₂ : m₂ ≤ m
inst✝ : SigmaFinite (μ.trim hle₂)
hf : StronglyMeasurable f
hindp : Indep m₁ m₂ μ
hfint : MemLp f 1 μ
s : Set Ω
hms : MeasurableSet s
hs : μ s < ∞
u v : Ω → E
a✝ : Disjoint (Function.support u) (Function.support v)
huint : MemLp u 1 μ
hvint : MemLp v 1 μ
hu : StronglyMeasurable u
hv : StronglyMeasurable v
hu_eq : μ.real s • ∫ (x : Ω), u x ∂μ = ∫ (x : Ω) in s, u x ∂μ
hv_eq : μ.real s • ∫ (x : Ω), v x ∂μ = ∫ (x : Ω) in s, v x ∂μ
⊢ μ.real s • ∫ (x : Ω), (u + v) x ∂μ = ∫ (x : Ω) in s, (u + v) x ∂μ
Module: Mathlib.Probability.Process.Stopping

Goal state before:
pp: Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝⁷ : Nonempty ι
τ : Ω → WithTop ι
inst✝⁶ : LinearOrder ι
inst✝⁵ : MeasurableSpace ι
inst✝⁴ : TopologicalSpace ι
inst✝³ : OrderTopology ι
inst✝² : SecondCountableTopology ι
inst✝¹ : BorelSpace ι
f : Filtration ι m
inst✝ : PseudoMetrizableSpace ι
hτ : IsStoppingTime f τ
i : ι
m_prod : MeasurableSpace (↑(Set.Iic i) × Ω) := Subtype.instMeasurableSpace.prod (↑f i)
m_set : (t : Set (↑(Set.Iic i) × Ω)) → MeasurableSpace ↑t := fun x ↦ Subtype.instMeasurableSpace
s : Set (↑(Set.Iic i) × Ω) := {p | τ p.2 ≤ ↑i}
hs : MeasurableSet s
h_meas_fst : ∀ (t : Set (↑(Set.Iic i) × Ω)), Measurable fun x ↦ ↑(↑x).1
j : ι
ω : ↑s
⊢ τ (↑ω).2 ≤ ↑j → τ (↑ω).2 ≤ ↑i

Tactic:
exact fun _ => ω.prop

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

Goal state before:
pp: case neg
Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝⁷ : Nonempty ι
τ : Ω → WithTop ι
inst✝⁶ : LinearOrder ι
inst✝⁵ : MeasurableSpace ι
inst✝⁴ : TopologicalSpace ι
inst✝³ : OrderTopology ι
inst✝² : SecondCountableTopology ι
inst✝¹ : BorelSpace ι
f : Filtration ι m
inst✝ : PseudoMetrizableSpace ι
hτ : IsStoppingTime f τ
i : ι
m_prod : MeasurableSpace (↑(Set.Iic i) × Ω) := Subtype.instMeasurableSpace.prod (↑f i)
m_set : (t : Set (↑(Set.Iic i) × Ω)) → MeasurableSpace ↑t := fun x ↦ Subtype.instMeasurableSpace
s : Set (↑(Set.Iic i) × Ω) := {p | τ p.2 ≤ ↑i}
hs : MeasurableSet s
h_meas_fst : ∀ (t : Set (↑(Set.Iic i) × Ω)), Measurable fun x ↦ ↑(↑x).1
sc : Set (↑(Set.Iic i) × Ω) := sᶜ
ω : ↑sc
hx_fst_le : ↑(↑ω).1 ≤ i
t : ι
ht : ↑t = τ (↑ω).2
⊢ i < t

Tactic:
convert! ω.prop

Goal state after:
Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝⁷ : Nonempty ι
τ : Ω → WithTop ι
inst✝⁶ : LinearOrder ι
inst✝⁵ : MeasurableSpace ι
inst✝⁴ : TopologicalSpace ι
inst✝³ : OrderTopology ι
inst✝² : SecondCountableTopology ι
inst✝¹ : BorelSpace ι
f : Filtration ι m
inst✝ : PseudoMetrizableSpace ι
hτ : IsStoppingTime f τ
i : ι
m_prod : MeasurableSpace (↑(Set.Iic i) × Ω) := ⋯
m_set : (t : Set (↑(Set.Iic i) × Ω)) → MeasurableSpace ↑t := ⋯
s : Set (↑(Set.Iic i) × Ω) := ⋯
hs : MeasurableSet s
h_meas_fst : ∀ (t : Set (↑(Set.Iic i) × Ω)), Measurable fun x ↦ ↑(↑x).1
sc : Set (↑(Set.Iic i) × Ω) := ⋯
ω : ↑sc
hx_fst_le : ↑(↑ω).1 ≤ i
t : ι
ht : ↑t = τ (↑ω).2
⊢ i < t ↔ ↑ω ∈ sc
Module: Mathlib.Probability.Moments.Basic

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
hX : AEMeasurable X μ
⊢ mgf id (Measure.map X μ) = mgf X μ

Tactic:
ext t

Goal state after:
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
hX : AEMeasurable X μ
t : ℝ
⊢ mgf id (Measure.map X μ) t = mgf X μ t
Module: Mathlib.Probability.Moments.Basic

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
X : Ω → ℝ
a b t : ℝ
hm : AEMeasurable X μ
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
ω : Ω
hl : a ≤ X ω
hr : X ω ≤ b
ht : 0 ≤ t
⊢ a * t ≤ t * X ω

Tactic:
by nlinarith

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

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
X : Ω → ℝ
a b t : ℝ
hm : AEMeasurable X μ
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
ω : Ω
hl : a ≤ X ω
hr : X ω ≤ b
ht : 0 ≤ t
⊢ t * X ω ≤ b * t

Tactic:
by nlinarith

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

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
X : Ω → ℝ
a b t : ℝ
hm : AEMeasurable X μ
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
ω : Ω
hl : a ≤ X ω
hr : X ω ≤ b
ht : ¬0 ≤ t
⊢ b * t ≤ t * X ω

Tactic:
by nlinarith

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

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
X : Ω → ℝ
a b t : ℝ
hm : AEMeasurable X μ
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
ω : Ω
hl : a ≤ X ω
hr : X ω ≤ b
ht : ¬0 ≤ t
⊢ t * X ω ≤ a * t

Tactic:
by nlinarith

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
hX : AEMeasurable X μ
⊢ complexMGF id (Measure.map X μ) = complexMGF X μ

Tactic:
ext t

Goal state after:
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
hX : AEMeasurable X μ
t : ℂ
⊢ complexMGF id (Measure.map X μ) t = complexMGF X μ t
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: case pos
μ : ℝ
v : ℝ≥0
hv : v = 0
⊢ Integrable (fun x ↦ (√(2 * π * ↑v))⁻¹ * rexp (-(x - μ) ^ 2 / (2 * ↑v))) ℙ

Tactic:
simp [hv]

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

Goal state before:
pp: case e'_6
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
h : ∀ (t : ℝ), Integrable (fun ω ↦ rexp (t * X ω)) μ
⊢ integrableExpSet X μ = Set.univ

Tactic:
ext t

Goal state after:
case e'_6
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
h : ∀ (t : ℝ), Integrable (fun ω ↦ rexp (t * X ω)) μ
t : ℝ
⊢ t ∈ integrableExpSet X μ ↔ t ∈ Set.univ
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)
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
⊢ (deriv (fun u ↦ ∫ (ω : Ω), X ω * rexp (u * X ω) ∂μ) v * mgf X μ v -
        (∫ (ω : Ω), X ω * rexp (v * X ω) ∂μ) * deriv (mgf X μ) v) /
      mgf X μ v ^ 2 =
    (deriv (fun u ↦ ∫ (ω : Ω), X ω * rexp (u * X ω) ∂μ) v * mgf X μ v -
        (∫ (ω : Ω), X ω * rexp (v * X ω) ∂μ) * ∫ (ω : Ω), X ω * rexp (v * X ω) ∂μ) /
      mgf X μ v ^ 2

Tactic:
by
    rw [deriv_mgf h]

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 : ℝ
ht : t ≠ 0
ht_int_pos : Integrable (fun ω ↦ rexp ((v + t) * X ω)) μ
ht_int_neg : Integrable (fun ω ↦ rexp ((v - t) * X ω)) μ
p : ℝ
hp : 0 ≤ p
hX : AEMeasurable X μ
⊢ Integrable (fun a ↦ ‖X a ^ p * rexp (v * X a)‖) μ

Tactic:
norm_eq_abs,

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

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
⊢ DifferentiableOn ℂ (complexMGF X μ) {z | z.re ∈ interior (integrableExpSet X μ)}

Tactic:
intro z hz

Goal state after:
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
z : ℂ
hz : z ∈ {z | z.re ∈ interior (integrableExpSet X μ)}
⊢ DifferentiableWithinAt ℂ (complexMGF X μ) {z | z.re ∈ interior (integrableExpSet X μ)} z
Module: Mathlib.Probability.Moments.IntegrableExpMul

Goal state before:
pp: l u v : ℝ
hv : v ∈ Set.Ioo l u
h_pos : 0 < min (v - l) (u - v)
⊢ v + min (v - l) (u - v) / 2 < v + min (v - l) (u - v)

Tactic:
gcongr; exact half_lt_self (by positivity)

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

Goal state before:
pp: l u v : ℝ
hv : v ∈ Set.Ioo l u
h_pos : 0 < min (v - l) (u - v)
⊢ v - min (v - l) (u - v) < v - min (v - l) (u - v) / 2

Tactic:
gcongr; exact half_lt_self (by positivity)

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
Ω' : Type u_3
mΩ' : MeasurableSpace Ω'
Y : Ω' → ℝ
μ' : Measure Ω'
hXY : mgf X μ = mgf Y μ'
hμμ' : μ = 0 ↔ μ' = 0
⊢ integrableExpSet X μ = integrableExpSet Y μ'

Tactic:
ext t

Goal state after:
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
Ω' : Type u_3
mΩ' : MeasurableSpace Ω'
Y : Ω' → ℝ
μ' : Measure Ω'
hXY : mgf X μ = mgf Y μ'
hμμ' : μ = 0 ↔ μ' = 0
t : ℝ
⊢ t ∈ integrableExpSet X μ ↔ t ∈ integrableExpSet Y μ'
Module: Mathlib.Probability.Moments.ComplexMGF

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
Ω' : Type u_3
mΩ' : MeasurableSpace Ω'
Y : Ω' → ℝ
μ' : Measure Ω'
hXY : mgf X μ = mgf Y μ'
hμμ' : μ = 0 ↔ μ' = 0
t : ℝ
⊢ t ∈ integrableExpSet X μ ↔ t ∈ integrableExpSet Y μ'

Tactic:
simp only [integrableExpSet, Set.mem_ofPred_eq]

Goal state after:
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
Ω' : Type u_3
mΩ' : MeasurableSpace Ω'
Y : Ω' → ℝ
μ' : Measure Ω'
hXY : mgf X μ = mgf Y μ'
hμμ' : μ = 0 ↔ μ' = 0
t : ℝ
⊢ Integrable (fun ω ↦ rexp (t * X ω)) μ ↔ Integrable (fun ω ↦ rexp (t * Y ω)) μ'
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
p : Measure Ω
μ : ℝ
v : ℝ≥0
X : Ω → ℝ
hX : Measure.map X p = gaussianReal μ v
t : ℝ
hX_meas : AEMeasurable X p
⊢ ↑(mgf X p t) = ↑(rexp (μ * t + ↑v * t ^ 2 / 2))

Tactic:
rw [← mgf_id_map hX_meas, ← complexMGF_ofReal, hX, complexMGF_id_gaussianReal, mul_comm μ]

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
p : Measure Ω
μ : ℝ
v : ℝ≥0
X : Ω → ℝ
hX : Measure.map X p = gaussianReal μ v
t : ℝ
hX_meas : AEMeasurable X p
⊢ cexp (↑t * ↑μ + ↑↑v * ↑t ^ 2 / 2) = ↑(rexp (t * μ + ↑v * t ^ 2 / 2))
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: μ : ℝ
v : ℝ≥0
⊢ mgf (fun x ↦ x) (gaussianReal μ v) = fun t ↦ rexp (μ * t + ↑v * t ^ 2 / 2)

Tactic:
ext t

Goal state after:
μ : ℝ
v : ℝ≥0
t : ℝ
⊢ mgf (fun x ↦ x) (gaussianReal μ v) t = rexp (μ * t + ↑v * t ^ 2 / 2)
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: μ : ℝ
v : ℝ≥0
⊢ (deriv fun t ↦ rexp (↑v * t ^ 2 / 2)) = fun t ↦ ↑v * t * rexp (↑v * t ^ 2 / 2)

Tactic:
ext t

Goal state after:
μ : ℝ
v : ℝ≥0
t : ℝ
⊢ deriv (fun t ↦ rexp (↑v * t ^ 2 / 2)) t = ↑v * t * rexp (↑v * t ^ 2 / 2)
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: m₁ m₂ : ℝ
v₁ v₂ : ℝ≥0
⊢ charFun (gaussianReal m₁ v₁ ∗ gaussianReal m₂ v₂) = charFun (gaussianReal (m₁ + m₂) (v₁ + v₂))

Tactic:
ext t

Goal state after:
m₁ m₂ : ℝ
v₁ v₂ : ℝ≥0
t : ℝ
⊢ charFun (gaussianReal m₁ v₁ ∗ gaussianReal m₂ v₂) t = charFun (gaussianReal (m₁ + m₂) (v₁ + v₂)) t
Module: Mathlib.Probability.Moments.CovarianceBilin

Goal state before:
pp: ι : Type u_2
Ω : Type u_3
inst✝¹ : Fintype ι
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
X : ι → Ω → ℝ
hX : ∀ (i : ι), MemLp (X i) 2 μ
i j : ι
this : ∀ (i : ι), AEMeasurable (X i) μ
⊢ ((covarianceBilin (Measure.map (fun ω ↦ toLp 2 fun x ↦ X x ω) μ)) ((basisFun ι ℝ) i)) ((basisFun ι ℝ) j) =
    cov[X i, X j; μ]

Tactic:
rw [covarianceBilin_apply_eq_cov, covariance_map]

Goal state after:
ι : Type u_2
Ω : Type u_3
inst✝¹ : Fintype ι
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
X : ι → Ω → ℝ
hX : ∀ (i : ι), MemLp (X i) 2 μ
i j : ι
this : ∀ (i : ι), AEMeasurable (X i) μ
⊢ cov[(fun u ↦ ⟪(basisFun ι ℝ) i, u⟫) ∘ fun ω ↦ toLp 2 fun x ↦ X x ω,
      (fun u ↦ ⟪(basisFun ι ℝ) j, u⟫) ∘ fun ω ↦ toLp 2 fun x ↦ X x ω; μ] =
    cov[X i, X j; μ]

case hX
ι : Type u_2
Ω : Type u_3
inst✝¹ : Fintype ι
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
X : ι → Ω → ℝ
hX : ∀ (i : ι), MemLp (X i) 2 μ
i j : ι
this : ∀ (i : ι), AEMeasurable (X i) μ
⊢ AEStronglyMeasurable (fun u ↦ ⟪(basisFun ι ℝ) i, u⟫) (Measure.map (fun ω ↦ toLp 2 fun x ↦ X x ω) μ)

case hY
ι : Type u_2
Ω : Type u_3
inst✝¹ : Fintype ι
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
X : ι → Ω → ℝ
hX : ∀ (i : ι), MemLp (X i) 2 μ
i j : ι
this : ∀ (i : ι), AEMeasurable (X i) μ
⊢ AEStronglyMeasurable (fun u ↦ ⟪(basisFun ι ℝ) j, u⟫) (Measure.map (fun ω ↦ toLp 2 fun x ↦ X x ω) μ)

case hZ
ι : Type u_2
Ω : Type u_3
inst✝¹ : Fintype ι
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
X : ι → Ω → ℝ
hX : ∀ (i : ι), MemLp (X i) 2 μ
i j : ι
this : ∀ (i : ι), AEMeasurable (X i) μ
⊢ AEMeasurable (fun ω ↦ toLp 2 fun x ↦ X x ω) μ

case h
ι : Type u_2
Ω : Type u_3
inst✝¹ : Fintype ι
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
X : ι → Ω → ℝ
hX : ∀ (i : ι), MemLp (X i) 2 μ
i j : ι
this : ∀ (i : ι), AEMeasurable (X i) μ
⊢ MemLp id 2 (Measure.map (fun ω ↦ toLp 2 fun x ↦ X x ω) μ)
Module: Mathlib.Probability.Distributions.Gaussian.Fernique

Goal state before:
pp: E : Type u_1
inst✝⁶ : NormedAddCommGroup E
inst✝⁵ : NormedSpace ℝ E
inst✝⁴ : MeasurableSpace E
inst✝³ : BorelSpace E
μ : Measure E
inst✝² : IsGaussian μ
inst✝¹ : SecondCountableTopology E
inst✝ : CompleteSpace E
hμ : ∀ (L : StrongDual ℝ E), ∫ (x : E), L x ∂μ = 0
θ : ℝ
L : StrongDual ℝ (E × E)
⊢ cexp
      (-(↑Var[⇑((L ∘SL ContinuousLinearMap.rotation θ) ∘SL ContinuousLinearMap.inl ℝ E E); μ] +
            ↑Var[⇑((L ∘SL ContinuousLinearMap.rotation θ) ∘SL ContinuousLinearMap.inr ℝ E E); μ]) /
        2) =
    cexp (-(↑Var[⇑(L ∘SL ContinuousLinearMap.inl ℝ E E); μ] + ↑Var[⇑(L ∘SL ContinuousLinearMap.inr ℝ E E); μ]) / 2)

Tactic:
congr 3

Goal state after:
case e_a
E : Type u_1
inst✝⁶ : NormedAddCommGroup E
inst✝⁵ : NormedSpace ℝ E
inst✝⁴ : MeasurableSpace E
inst✝³ : BorelSpace E
μ : Measure E
inst✝² : IsGaussian μ
inst✝¹ : SecondCountableTopology E
inst✝ : CompleteSpace E
hμ : ∀ (L : StrongDual ℝ E), ∫ (x : E), L x ∂μ = 0
θ : ℝ
L : StrongDual ℝ (E × E)
⊢ ↑Var[⇑((L ∘SL ContinuousLinearMap.rotation θ) ∘SL ContinuousLinearMap.inl ℝ E E); μ] +
      ↑Var[⇑((L ∘SL ContinuousLinearMap.rotation θ) ∘SL ContinuousLinearMap.inr ℝ E E); μ] =
    ↑Var[⇑(L ∘SL ContinuousLinearMap.inl ℝ E E); μ] + ↑Var[⇑(L ∘SL ContinuousLinearMap.inr ℝ E E); μ]
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
x✝ : ∃ m f, f.toBilinForm.IsPosSemidef ∧ ∀ (L : StrongDual ℝ E), charFunDual μ L = cexp (↑(L m) * I - ↑((f L) L) / 2)
m : E
f : StrongDual ℝ E →L[ℝ] StrongDual ℝ E →L[ℝ] ℝ
hf : f.toBilinForm.IsPosSemidef
h : ∀ (L : StrongDual ℝ E), charFunDual μ L = cexp (↑(L m) * I - ↑((f L) L) / 2)
L : E →L[ℝ] ℝ
⊢ charFun (Measure.map (⇑L) μ) = charFun (gaussianReal (L m) ((f L) L).toNNReal)

Tactic:
ext t

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
x✝ : ∃ m f, f.toBilinForm.IsPosSemidef ∧ ∀ (L : StrongDual ℝ E), charFunDual μ L = cexp (↑(L m) * I - ↑((f L) L) / 2)
m : E
f : StrongDual ℝ E →L[ℝ] StrongDual ℝ E →L[ℝ] ℝ
hf : f.toBilinForm.IsPosSemidef
h : ∀ (L : StrongDual ℝ E), charFunDual μ L = cexp (↑(L m) * I - ↑((f L) L) / 2)
L : E →L[ℝ] ℝ
t : ℝ
⊢ charFun (Measure.map (⇑L) μ) t = charFun (gaussianReal (L m) ((f L) L).toNNReal) t
Module: Mathlib.Probability.Distributions.Gaussian.Multivariate

Goal state before:
pp: ι : Type u_1
inst✝ : Fintype ι
⊢ charFun (Measure.map (toLp 2) (Measure.pi fun x ↦ gaussianReal 0 1)) = charFun (stdGaussian (EuclideanSpace ℝ ι))

Tactic:
ext t

Goal state after:
ι : Type u_1
inst✝ : Fintype ι
t : EuclideanSpace ℝ ι
⊢ charFun (Measure.map (toLp 2) (Measure.pi fun x ↦ gaussianReal 0 1)) t = charFun (stdGaussian (EuclideanSpace ℝ ι)) t
Module: Mathlib.Probability.Distributions.Gaussian.Multivariate

Goal state before:
pp: case ha
ι : Type u_1
inst✝¹ : Fintype ι
inst✝ : DecidableEq ι
μ : EuclideanSpace ℝ ι
S : Matrix ι ι ℝ
hS : ¬S.PosSemidef
⊢ ¬0 ≤ S

Tactic:
change ¬ (S - 0).PosSemidef

Goal state after:
case ha
ι : Type u_1
inst✝¹ : Fintype ι
inst✝ : DecidableEq ι
μ : EuclideanSpace ℝ ι
S : Matrix ι ι ℝ
hS : ¬S.PosSemidef
⊢ ¬(S - 0).PosSemidef
Module: Mathlib.Probability.Distributions.Gaussian.CharFun

Goal state before:
pp: E : Type u_1
inst✝⁷ : NormedAddCommGroup E
inst✝⁶ : SecondCountableTopology E
inst✝⁵ : CompleteSpace E
inst✝⁴ : MeasurableSpace E
inst✝³ : BorelSpace E
μ : Measure E
inst✝² : InnerProductSpace ℝ E
ν : Measure E
inst✝¹ : IsGaussian μ
inst✝ : IsGaussian ν
hm : ∫ (x : E), id x ∂μ = ∫ (x : E), id x ∂ν
hv : covarianceBilin μ = covarianceBilin ν
⊢ charFun μ = charFun ν

Tactic:
ext t

Goal state after:
E : Type u_1
inst✝⁷ : NormedAddCommGroup E
inst✝⁶ : SecondCountableTopology E
inst✝⁵ : CompleteSpace E
inst✝⁴ : MeasurableSpace E
inst✝³ : BorelSpace E
μ : Measure E
inst✝² : InnerProductSpace ℝ E
ν : Measure E
inst✝¹ : IsGaussian μ
inst✝ : IsGaussian ν
hm : ∫ (x : E), id x ∂μ = ∫ (x : E), id x ∂ν
hv : covarianceBilin μ = covarianceBilin ν
t : E
⊢ charFun μ t = charFun ν t
Module: Mathlib.Probability.Independence.Process.HasIndepIncrements.Basic

Goal state before:
pp: case inr
T : Type u_1
Ω : Type u_2
E : Type u_3
mΩ : MeasurableSpace Ω
P : Measure Ω
X : T → Ω → E
inst✝² : Preorder T
inst✝¹ : MeasurableSpace E
inst✝ : Sub E
hX : HasIndepIncrements X P
t : ℕ → T
ht : Monotone t
s : Finset ℕ
hs : s.Nonempty
⊢ iIndepFun (s.restrict fun i ω ↦ X (t (i + 1)) ω - X (t i) ω) P

Tactic:
let g (x : s) : Fin (s.max' hs + 1) := ⟨x.1, Nat.lt_add_one_of_le (s.le_max' x.1 x.2)⟩
    refine iIndepFun.precomp (g := g) ?_ (hX (s.max' hs + 1) (fun m ↦ t m) ?_)
    · simp [g, Function.Injective]
    · exact ht.comp Fin.val_strictMono.monotone

Goal state after:
Module: Mathlib.Probability.Independence.CharacteristicFunction

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁴ : IsFiniteMeasure P
E : Type u_2
mE : MeasurableSpace E
inst✝³ : NormedAddCommGroup E
inst✝² : BorelSpace E
inst✝¹ : SecondCountableTopology E
X : Ω → E
inst✝ : InnerProductSpace ℝ E
Y : Ω → E
mX : AEMeasurable X P
mY : AEMeasurable Y P
hXY : X ⟂ᵢ[P] Y
⊢ charFun (Measure.map (X + Y) P) = charFun (Measure.map X P) * charFun (Measure.map Y P)

Tactic:
ext t

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝⁴ : IsFiniteMeasure P
E : Type u_2
mE : MeasurableSpace E
inst✝³ : NormedAddCommGroup E
inst✝² : BorelSpace E
inst✝¹ : SecondCountableTopology E
X : Ω → E
inst✝ : InnerProductSpace ℝ E
Y : Ω → E
mX : AEMeasurable X P
mY : AEMeasurable Y P
hXY : X ⟂ᵢ[P] Y
t : E
⊢ charFun (Measure.map (X + Y) P) t = (charFun (Measure.map X P) * charFun (Measure.map Y P)) t
Module: Mathlib.Probability.Process.FiniteDimensionalLaws

Goal state before:
pp: T : Type u_1
Ω : Type u_2
𝓧 : T → Type u_3
mΩ : MeasurableSpace Ω
mα : (t : T) → MeasurableSpace (𝓧 t)
X Y : (t : T) → Ω → 𝓧 t
P : Measure Ω
inst✝ : IsFiniteMeasure P
hX : AEMeasurable (fun ω x ↦ X x ω) P
hY : AEMeasurable (fun ω x ↦ Y x ω) P
h : Measure.map (fun ω x ↦ X x ω) P = Measure.map (fun ω x ↦ Y x ω) P
I : Finset T
⊢ Measure.map (fun ω ↦ I.restrict fun x ↦ X x ω) P = Measure.map I.restrict (Measure.map (fun ω x ↦ X x ω) P)

Tactic:
rw [AEMeasurable.map_map_of_aemeasurable (by fun_prop) hX, Function.comp_def]

Goal state after: