mathlib-initiative/mathlib-tactics huggingface dataset:
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✝ : IsFiniteMeasure μ
f : ℕ → Ω → E
hadp : StronglyAdapted 𝒢 f
hint : ∀ (i : ℕ), Integrable (f i) μ
hf : ∀ (i : ℕ), f i =ᵐ[μ] μ[f (i + 1) | ↑𝒢 i]
i j : ℕ
hij : i ≤ j
⊢ μ[f j | ↑𝒢 i] =ᵐ[μ] f i

Tactic:
induction j, hij using Nat.le_induction with
  | base =>
    refine ae_of_all _ fun _ ↦ ?_
    rw [condExp_of_stronglyMeasurable (𝒢.le i) (hadp i) (hint i)]
  | succ k hik hk =>
    filter_upwards [hk, condExp_congr_ae (hf k), 𝒢.condExp_condExp (f (k + 1)) hik]
      with ω hω1 hω2 hω3
    rw [← hω1, hω2, hω3]

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
n : ι
ω : Ω
m i : ι
hi : i ≤ m
h : ¬∃ j ∈ Set.Icc n m, u j ω ∈ s
h' : m < i
⊢ False

Tactic:
← not_le

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
n : ι
ω : Ω
m i : ι
hi : i ≤ m
h : ¬∃ j ∈ Set.Icc n m, u j ω ∈ s
h' : ¬i ≤ m
⊢ False
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
n i : ℕ
x✝ : i ∈ Finset.range N
hl : MeasurableSet {ω | lowerCrossingTime a b f N i ω ≤ n}
hu : MeasurableSet {ω | upperCrossingTime a b f N (i + 1) ω ≤ n}
⊢ MeasurableSet fun ω ↦ ({n | lowerCrossingTime a b f N i ω ≤ n} ∩ {n | n < upperCrossingTime a b f N (i + 1) ω}) n

Tactic:
← not_le

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
n i : ℕ
x✝ : i ∈ Finset.range N
hl : MeasurableSet {ω | lowerCrossingTime a b f N i ω ≤ n}
hu : MeasurableSet {ω | upperCrossingTime a b f N (i + 1) ω ≤ n}
⊢ MeasurableSet fun ω ↦ ({n | lowerCrossingTime a b f N i ω ≤ n} ∩ {n | ¬upperCrossingTime a b f N (i + 1) ω ≤ n}) n
Module: Mathlib.Probability.Process.HittingTime

Goal state before:
pp: case mp
Ω : Type u_1
β : Type u_2
ι : Type u_3
inst✝ : ConditionallyCompleteLinearOrder ι
u : ι → Ω → β
s : Set β
n i : ι
ω : Ω
h_top : hittingAfter u s n ω ≠ ⊤
h_exists : ∃ j, n ≤ j ∧ u j ω ∈ s
h_le : ↑n ≤ hittingAfter u s n ω
j : ι
hj₁ : n ≤ j ∧ u j ω ∈ s
hj₂ : j < i
⊢ ∃ j ∈ Set.Ico n i, u j ω ∈ s

Tactic:
exact ⟨j, ⟨hj₁.1, hj₂⟩, hj₁.2⟩

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✝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 = ⊥
⊢ MeasurableSet {ω | τ ω ≤ ↑t}

Tactic:
have h_eq : {ω | τ ω ≤ t} = {ω | τ ω < t} ∪ {ω | τ ω = t} := by ext; grind

Goal state after:
case pos
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 = ⊥
h_eq : {ω | τ ω ≤ ↑t} = {ω | τ ω < ↑t} ∪ {ω | τ ω = ↑t}
⊢ MeasurableSet {ω | τ ω ≤ ↑t}
Module: Mathlib.Probability.Process.HittingTime

Goal state before:
pp: Ω : Type u_1
β : Type u_2
ι : Type u_3
m : MeasurableSpace Ω
inst✝⁶ : ConditionallyCompleteLinearOrder ι
inst✝⁵ : WellFoundedLT ι
inst✝⁴ : Countable ι
inst✝³ : TopologicalSpace ι
inst✝² : OrderTopology ι
inst✝¹ : FirstCountableTopology ι
inst✝ : MeasurableSpace β
f : Filtration ι m
u : ι → Ω → β
τ : Ω → WithTop ι
hτ : IsStoppingTime f τ
N : ι
hτbdd : ∀ (x : Ω), τ x ≤ ↑N
s : Set β
hs : MeasurableSet s
hf : Adapted f u
⊢ IsStoppingTime f fun x ↦ ↑(hittingBtwn u s (τ x).untopA N x)

Tactic:
by
  intro n
  have h₁ : {x | hittingBtwn u s (τ x).untopA N x ≤ n} =
    (⋃ i ≤ n, {x | τ x = i} ∩ {x | hittingBtwn u s i N x ≤ n}) ∪
      ⋃ i > n, {x | τ x = i} ∩ {x | hittingBtwn u s i N x ≤ n} := by
    ext x
    simp only [Set.mem_ofPred_eq, gt_iff_lt, Set.mem_union, Set.mem_iUnion, Set.mem_inter_iff,
      exists_and_left, exists_prop]
    specialize hτbdd x
    have h_top : τ x ≠ ⊤ := fun h => by simp [h] at hτbdd
    lift τ x to ι using h_top with t
    simp [← or_and_right, le_or_gt]
  have h₂ : ⋃ i > n, {x | τ x = i} ∩ {x | hittingBtwn u s i N x ≤ n} = ∅ := by
    ext x
    simp only [gt_iff_lt, Set.mem_iUnion, Set.mem_inter_iff, Set.mem_ofPred_eq, exists_prop,
      Set.mem_empty_iff_false, iff_false, not_exists, not_and, not_le]
    refine fun m hm hτ ↦ hm.trans_le <| le_hittingBtwn ?_ x
    specialize hτbdd x
    have h_top : τ x ≠ ⊤ := fun h => by simp [h] at hτbdd
    lift τ x to ι using h_top with t
    rw [hτ] at hτbdd
    exact mod_cast hτbdd
  simp only [WithTop.coe_le_coe, h₁, h₂, Set.union_empty]
  refine MeasurableSet.iUnion fun i => MeasurableSet.iUnion fun hi =>
    (f.mono hi _ (hτ.measurableSet_eq i)).inter ?_
  simpa using hf.isStoppingTime_hittingBtwn hs n

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✝ : 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:
simp only [hij, Set.ofPred_false, Set.inter_empty, @MeasurableSet.empty _ (f.1 j)]

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

Goal state before:
pp: case empty
Ω : Type u_1
m0 : MeasurableSpace Ω
μ : Measure Ω
ℱ : Filtration ℕ m0
inst✝ : IsFiniteMeasure μ
g : Ω → ℝ
hg : Integrable g μ
hgmeas : StronglyMeasurable g
hle : ⨆ n, ↑ℱ n ≤ m0
hunif : UniformIntegrable (fun n ↦ μ[g | ↑ℱ n]) 1 μ
R : ℝ≥0
hR : ∀ (i : ℕ), eLpNorm ((fun n ↦ μ[g | ↑ℱ n]) i) 1 μ ≤ ↑R
hlimint : Integrable (limitProcess (fun n ↦ μ[g | ↑ℱ n]) ℱ μ) μ
this :
  ∀ (n : ℕ) (s : Set Ω),
    MeasurableSet s → ∫ (x : Ω) in s, g x ∂μ = ∫ (x : Ω) in s, limitProcess (fun n x ↦ μ[g | ↑ℱ n] x) ℱ μ x ∂μ
s : Set Ω
hpi : IsPiSystem {s | ∃ n, MeasurableSet s}
x✝ : μ ∅ < ∞
⊢ ∫ (x : Ω) in ∅, g x ∂μ = ∫ (x : Ω) in ∅, limitProcess (fun n x ↦ μ[g | ↑ℱ n] x) ℱ μ x ∂μ

Tactic:
simp only [Measure.restrict_empty, integral_zero_measure]

Goal state after:
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
hg : Integrable g μ
hgmeas : StronglyMeasurable g
⊢ ∀ᵐ (x : Ω) ∂μ, Tendsto (fun n ↦ μ[g | ↑ℱ n] x) atTop (𝓝 (g x))

Tactic:
have hle : ⨆ n, ℱ n ≤ m0 := sSup_le fun m ⟨n, hn⟩ => hn ▸ ℱ.le _
  have hunif : UniformIntegrable (fun n => μ[g | ℱ n]) 1 μ :=
    hg.uniformIntegrable_condExp_filtration
  obtain ⟨R, hR⟩ := hunif.2.2
  have hlimint : Integrable (ℱ.limitProcess (fun n => μ[g | ℱ n]) μ) μ :=
    (memLp_limitProcess_of_eLpNorm_bdd hunif.1 hR).integrable le_rfl
  suffices g =ᵐ[μ] ℱ.limitProcess (fun n x => (μ[g | ℱ n]) x) μ by
    filter_upwards [this, (martingale_condExp g ℱ μ).submartingale.ae_tendsto_limitProcess hR] with
      x heq ht
    rwa [heq]
  have : ∀ n s, MeasurableSet[ℱ n] s →
      ∫ x in s, g x ∂μ = ∫ x in s, ℱ.limitProcess (fun n x => (μ[g | ℱ n]) x) μ x ∂μ := by
    intro n s hs
    rw [← setIntegral_condExp (ℱ.le n) hg hs, ← setIntegral_condExp (ℱ.le n) hlimint hs]
    refine setIntegral_congr_ae (ℱ.le _ _ hs) ?_
    filter_upwards [(martingale_condExp g ℱ μ).ae_eq_condExp_limitProcess hunif n] with x hx _
    rw [hx]
  refine ae_eq_of_forall_setIntegral_eq_of_sigmaFinite' hle (fun s _ _ => hg.integrableOn)
    (fun s _ _ => hlimint.integrableOn) (fun s hs _ => ?_) hgmeas.aestronglyMeasurable
    stronglyMeasurable_limitProcess.aestronglyMeasurable
  have hpi : IsPiSystem {s | ∃ n, MeasurableSet[ℱ n] s} := by
    rw [Set.ofPred_exists]
    exact isPiSystem_iUnion_of_monotone _ (fun n ↦ (ℱ n).isPiSystem_measurableSet) fun _ _ ↦ ℱ.mono
  induction s, hs
    using MeasurableSpace.induction_on_inter (MeasurableSpace.measurableSpace_iSup_eq ℱ) hpi with
  | empty =>
    simp only [Measure.restrict_empty, integral_zero_measure]
  | basic s hs =>
    rcases hs with ⟨n, hn⟩
    exact this n _ hn
  | compl t htmeas ht =>
    have hgeq := @setIntegral_compl _ _ (⨆ n, ℱ n) _ _ _ _ _ htmeas (hg.trim hle hgmeas)
    have hheq := @setIntegral_compl _ _ (⨆ n, ℱ n) _ _ _ _ _ htmeas
      (hlimint.trim hle stronglyMeasurable_limitProcess)
    rw [setIntegral_trim hle hgmeas htmeas.compl,
      setIntegral_trim hle stronglyMeasurable_limitProcess htmeas.compl, hgeq, hheq, ←
      setIntegral_trim hle hgmeas htmeas, ←
      setIntegral_trim hle stronglyMeasurable_limitProcess htmeas, ← integral_trim hle hgmeas, ←
      integral_trim hle stronglyMeasurable_limitProcess, ← setIntegral_univ,
      this 0 _ MeasurableSet.univ, setIntegral_univ, ht (measure_lt_top _ _)]
  | iUnion f hf hfmeas heq =>
    rw [integral_iUnion (fun n => hle _ (hfmeas n)) hf hg.integrableOn,
      integral_iUnion (fun n => hle _ (hfmeas n)) hf hlimint.integrableOn]
    exact tsum_congr fun n => heq _ (measure_lt_top _ _)

Goal state after:
Module: Mathlib.Probability.BorelCantelli

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

Tactic:
rintro ⟨B, hB⟩
    refine not_eventually.2 (Frequently.of_forall fun n => ?_) (htends B.toNNReal)
    rw [mem_upperBounds] at hB
    specialize hB (∑ k ∈ Finset.range n, μ (s (k + 1))).toReal _
    · refine ⟨n, ?_⟩
      rw [ENNReal.toReal_sum (by finiteness)]
      rfl
    · rwa [not_lt, ENNReal.ofNNReal_toNNReal, ENNReal.le_ofReal_iff_toReal_le]
      · simp
      · exact le_trans (by positivity) hB

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

Goal state before:
pp: Ω : Type u_2
s : ℕ → Set Ω
ω : Ω
n : ℕ
⊢ (s (n + 1)).indicator (fun a ↦ ‖1 a‖) ω ≤ 1

Tactic:
refine Set.indicator_le' (fun _ _ => ?_) (fun _ _ => zero_le_one) _

Goal state after:
Ω : Type u_2
s : ℕ → Set Ω
ω : Ω
n : ℕ
x✝¹ : Ω
x✝ : x✝¹ ∈ s (n + 1)
⊢ ‖1 x✝¹‖ ≤ 1
Module: Mathlib.Probability.Process.Stopping

Goal state before:
pp: Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝² : Nonempty ι
μ : Measure Ω
τ : Ω → WithTop ι
E : Type u_4
p : ℝ≥0∞
u : ι → Ω → E
inst✝¹ : PartialOrder ι
ℱ : Filtration ι m
inst✝ : NormedAddCommGroup E
hτ : IsStoppingTime ℱ τ
hu : ∀ (n : ι), MemLp (u n) p μ
s : Finset ι
hbdd : ∀ (ω : Ω), τ ω ∈ WithTop.some '' ↑s
⊢ MemLp (∑ i ∈ s, {ω | τ ω = ↑i}.indicator (u i)) p μ

Tactic:
refine memLp_finsetSum' _ fun i _ => MemLp.indicator ?_ (hu i)

Goal state after:
Ω : Type u_1
ι : Type u_3
m : MeasurableSpace Ω
inst✝² : Nonempty ι
μ : Measure Ω
τ : Ω → WithTop ι
E : Type u_4
p : ℝ≥0∞
u : ι → Ω → E
inst✝¹ : PartialOrder ι
ℱ : Filtration ι m
inst✝ : NormedAddCommGroup E
hτ : IsStoppingTime ℱ τ
hu : ∀ (n : ι), MemLp (u n) p μ
s : Finset ι
hbdd : ∀ (ω : Ω), τ ω ∈ WithTop.some '' ↑s
i : ι
x✝ : i ∈ s
⊢ MeasurableSet {ω | τ ω = ↑i}
Module: Mathlib.Probability.Process.Stopping

Goal state before:
pp: Ω : Type u_1
β : Type u_2
u : ℕ → Ω → β
τ π : Ω → ℕ∞
inst✝ : AddCommGroup β
hle : τ ≤ π
N : ℕ
hbdd : ∀ (ω : Ω), π ω ≤ ↑N
hπ_top : ∀ (ω : Ω), π ω ≠ ⊤
⊢ stoppedValue u π - stoppedValue u τ = fun ω ↦
    (∑ i ∈ Finset.range (N + 1), {ω | τ ω ≤ ↑i ∧ ↑i < π ω}.indicator (u (i + 1) - u i)) ω

Tactic:
have hτ_top ω : τ ω ≠ ⊤ := ne_top_of_le_ne_top (hπ_top ω) (mod_cast hle ω)

Goal state after:
Ω : Type u_1
β : Type u_2
u : ℕ → Ω → β
τ π : Ω → ℕ∞
inst✝ : AddCommGroup β
hle : τ ≤ π
N : ℕ
hbdd : ∀ (ω : Ω), π ω ≤ ↑N
hπ_top : ∀ (ω : Ω), π ω ≠ ⊤
hτ_top : ∀ (ω : Ω), τ ω ≠ ⊤
⊢ stoppedValue u π - stoppedValue u τ = fun ω ↦
    (∑ i ∈ Finset.range (N + 1), {ω | τ ω ≤ ↑i ∧ ↑i < π ω}.indicator (u (i + 1) - u i)) ω
Module: Mathlib.Probability.Process.Stopping

Goal state before:
pp: Ω : Type u_1
β : Type u_2
u : ℕ → Ω → β
τ π : Ω → ℕ∞
inst✝ : AddCommGroup β
hle : τ ≤ π
N : ℕ
hbdd : ∀ (ω : Ω), π ω ≤ ↑N
⊢ stoppedValue u π - stoppedValue u τ = fun ω ↦
    (∑ i ∈ Finset.range (N + 1), {ω | τ ω ≤ ↑i ∧ ↑i < π ω}.indicator (u (i + 1) - u i)) ω

Tactic:
by
  have hπ_top ω : π ω ≠ ⊤ := fun h ↦ by specialize hbdd ω; simp [h] at hbdd
  have hτ_top ω : τ ω ≠ ⊤ := ne_top_of_le_ne_top (hπ_top ω) (mod_cast hle ω)
  rw [stoppedValue_sub_eq_sum hle]
  swap; · intro ω; exact mod_cast hπ_top ω
  ext ω
  simp only [Finset.sum_apply, Finset.sum_indicator_eq_sum_filter]
  refine Finset.sum_congr ?_ fun _ _ => rfl
  ext i
  simp only [Set.mem_ofPred_eq, Finset.mem_Ico]
  specialize hbdd ω
  lift τ ω to ℕ using hτ_top ω with t ht
  lift π ω to ℕ using hπ_top ω with b hb
  simp only [Nat.cast_le] at hbdd
  simp
  grind

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

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

Tactic:
by
      rw [this]
      refine (integrable_exp_neg_mul_sq ?_).const_mul (√(2 * π * v))⁻¹
      simpa [pos_iff_ne_zero]

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

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
h : 0 ∈ interior (integrableExpSet X μ)
⊢ deriv (mgf X μ) 0 = ∫ (x : Ω), X x ∂μ

Tactic:
simp [deriv_mgf h]

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
⊢ deriv (fun u ↦ ∫ (ω : Ω), X ω * rexp (u * X ω) ∂μ) v * (mgf X μ v / mgf X μ v) / mgf X μ v =
    deriv (fun u ↦ ∫ (ω : Ω), X ω * rexp (u * X ω) ∂μ) v / mgf X μ v

Tactic:
div_self,

Goal state after:
case e_a
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
v : ℝ
h : v ∈ interior (integrableExpSet X μ)
hμ : ¬μ = 0
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 * 1 / mgf X μ v =
    deriv (fun u ↦ ∫ (ω : Ω), X ω * rexp (u * X ω) ∂μ) v / mgf X μ v

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
⊢ mgf X μ v ≠ 0
Module: Mathlib.Probability.Moments.ComplexMGF

Goal state before:
pp: case neg.refine_1.left
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
ht : t ∈ interior (integrableExpSet Y μ')
hX : AnalyticOnNhd ℂ (complexMGF X μ) {z | z.re ∈ interior (integrableExpSet Y μ')}
hY : AnalyticOnNhd ℂ (complexMGF Y μ') {z | z.re ∈ interior (integrableExpSet Y μ')}
xs : ℕ → ℝ
hx_tendsto : Tendsto xs atTop (𝓝 t) ∧ ∀ᶠ (n : ℕ) in atTop, xs n ∈ {t}ᶜ
hx_eq : ∀ (n : ℕ), complexMGF X μ ↑(xs n) = complexMGF Y μ' ↑(xs n)
⊢ Tendsto (fun n ↦ ↑(xs n)) atTop (𝓝 ↑t)

Tactic:
rw [tendsto_ofReal_iff]
      exact hx_tendsto.1

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

Goal state before:
pp: E : Type u_1
F : Type u_2
inst✝⁷ : NormedAddCommGroup E
inst✝⁶ : NormedSpace ℝ E
inst✝⁵ : MeasurableSpace E
inst✝⁴ : BorelSpace E
inst✝³ : NormedAddCommGroup F
inst✝² : NormedSpace ℝ F
inst✝¹ : MeasurableSpace F
inst✝ : BorelSpace F
μ : Measure E
L : E ≃L[ℝ] F
h : IsGaussian (Measure.map (⇑L) μ)
this : μ = Measure.map (⇑L.symm) (Measure.map (⇑L) μ)
⊢ IsGaussian μ

Tactic:
rw [this]; infer_instance

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

Goal state before:
pp: E : Type u_1
inst✝¹¹ : NormedAddCommGroup E
inst✝¹⁰ : InnerProductSpace ℝ E
inst✝⁹ : MeasurableSpace E
inst✝⁸ : BorelSpace E
μ : Measure E
F : Type u_2
inst✝⁷ : NormedAddCommGroup F
inst✝⁶ : InnerProductSpace ℝ F
inst✝⁵ : MeasurableSpace F
inst✝⁴ : BorelSpace F
inst✝³ : SecondCountableTopology F
inst✝² : CompleteSpace F
inst✝¹ : CompleteSpace E
inst✝ : IsFiniteMeasure μ
h : MemLp id 2 μ
L : E →L[ℝ] F
u v : F
⊢ ∫ (x : E), ⟪u, L x - L (∫ (x : E), x ∂μ)⟫ * ⟪v, L x - L (∫ (x : E), x ∂μ)⟫ ∂μ =
    ∫ (z : E),
      ⟪(ContinuousLinearMap.adjoint L) u, z - ∫ (x : E), x ∂μ⟫ *
        ⟪(ContinuousLinearMap.adjoint L) v, z - ∫ (x : E), x ∂μ⟫ ∂μ

pp: case hφ
E : Type u_1
inst✝¹¹ : NormedAddCommGroup E
inst✝¹⁰ : InnerProductSpace ℝ E
inst✝⁹ : MeasurableSpace E
inst✝⁸ : BorelSpace E
μ : Measure E
F : Type u_2
inst✝⁷ : NormedAddCommGroup F
inst✝⁶ : InnerProductSpace ℝ F
inst✝⁵ : MeasurableSpace F
inst✝⁴ : BorelSpace F
inst✝³ : SecondCountableTopology F
inst✝² : CompleteSpace F
inst✝¹ : CompleteSpace E
inst✝ : IsFiniteMeasure μ
h : MemLp id 2 μ
L : E →L[ℝ] F
u v : F
⊢ AEMeasurable (⇑L) μ

pp: case hfm
E : Type u_1
inst✝¹¹ : NormedAddCommGroup E
inst✝¹⁰ : InnerProductSpace ℝ E
inst✝⁹ : MeasurableSpace E
inst✝⁸ : BorelSpace E
μ : Measure E
F : Type u_2
inst✝⁷ : NormedAddCommGroup F
inst✝⁶ : InnerProductSpace ℝ F
inst✝⁵ : MeasurableSpace F
inst✝⁴ : BorelSpace F
inst✝³ : SecondCountableTopology F
inst✝² : CompleteSpace F
inst✝¹ : CompleteSpace E
inst✝ : IsFiniteMeasure μ
h : MemLp id 2 μ
L : E →L[ℝ] F
u v : F
⊢ AEStronglyMeasurable (fun z ↦ ⟪u, z - L (∫ (x : E), x ∂μ)⟫ * ⟪v, z - L (∫ (x : E), x ∂μ)⟫) (Measure.map (⇑L) μ)

Tactic:
· simp_rw [← map_sub, ← L.adjoint_inner_left]

Goal state after:
case hφ
E : Type u_1
inst✝¹¹ : NormedAddCommGroup E
inst✝¹⁰ : InnerProductSpace ℝ E
inst✝⁹ : MeasurableSpace E
inst✝⁸ : BorelSpace E
μ : Measure E
F : Type u_2
inst✝⁷ : NormedAddCommGroup F
inst✝⁶ : InnerProductSpace ℝ F
inst✝⁵ : MeasurableSpace F
inst✝⁴ : BorelSpace F
inst✝³ : SecondCountableTopology F
inst✝² : CompleteSpace F
inst✝¹ : CompleteSpace E
inst✝ : IsFiniteMeasure μ
h : MemLp id 2 μ
L : E →L[ℝ] F
u v : F
⊢ AEMeasurable (⇑L) μ

case hfm
E : Type u_1
inst✝¹¹ : NormedAddCommGroup E
inst✝¹⁰ : InnerProductSpace ℝ E
inst✝⁹ : MeasurableSpace E
inst✝⁸ : BorelSpace E
μ : Measure E
F : Type u_2
inst✝⁷ : NormedAddCommGroup F
inst✝⁶ : InnerProductSpace ℝ F
inst✝⁵ : MeasurableSpace F
inst✝⁴ : BorelSpace F
inst✝³ : SecondCountableTopology F
inst✝² : CompleteSpace F
inst✝¹ : CompleteSpace E
inst✝ : IsFiniteMeasure μ
h : MemLp id 2 μ
L : E →L[ℝ] F
u v : F
⊢ AEStronglyMeasurable (fun z ↦ ⟪u, z - L (∫ (x : E), x ∂μ)⟫ * ⟪v, z - L (∫ (x : E), x ∂μ)⟫) (Measure.map (⇑L) μ)
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 : ‖p.1 + p.2‖ - ‖p.1 - p.2‖ ≤ 2 * min ‖p.1‖ ‖p.2‖
⊢ (‖p.1 + p.2‖ - ‖p.1 - p.2‖) / 2 ≤ min ‖p.1‖ ‖p.2‖

Tactic:
linarith

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

Goal state before:
pp: case h₁
E : Type u_1
inst✝¹ : NormedAddCommGroup E
mE : MeasurableSpace E
μ : Measure E
inst✝ : NormedSpace ℝ E
c : E := ⋯
this✝¹ : 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✝ : ‖y‖ ≤ ‖c‖ / 2
this : ‖c‖ ≤ ‖y‖ + ‖y - c‖
⊢ ‖c‖ ≤ 2 * ‖y - c‖

Tactic:
linarith

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

Goal state before:
pp: case neg.inr.inr.inl
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✝¹ : 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✝ : ‖y‖ ≤ ‖c‖ / 2
this : ‖c‖ ≤ ‖y‖ + ‖y - c‖
⊢ ‖c‖ ^ ↑p ≤ 2 * ‖c‖ ^ (↑p - 1) * ‖y‖ + 2 ^ ↑p * ‖y - c‖ ^ ↑p

Tactic:
calc ‖c‖ ^ (p : ℝ)
    _ ≤ (2 * ‖y - c‖) ^ (p : ℝ) := by
      gcongr
      linarith
    _ = 0 + 2 ^ (p : ℝ) * ‖y - c‖ ^ (p : ℝ) := by
      rw [Real.mul_rpow (by simp) (by positivity)]
      ring
    _ ≤ 2 * ‖c‖ ^ (p - 1 : ℝ) * ‖y‖ + 2 ^ (p : ℝ) * ‖y - c‖ ^ (p : ℝ) := by
      gcongr
      positivity

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

Goal state before:
pp: case hbc
E : Type u_1
inst✝¹ : NormedAddCommGroup E
mE : MeasurableSpace E
μ : Measure E
inst✝ : NormedSpace ℝ E
c : E := ⋯
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‖
⊢ ‖c‖ ≤ 2 * ‖y‖

Tactic:
linarith

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 / (1 - c).toReal

Tactic:
one_le_div

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).toReal ≤ c.toReal

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
⊢ ∫⁻ (x : E) in closedBall 0 a, ENNReal.ofReal (rexp (C * ‖x‖ ^ 2)) ∂μ ≤
    ∫⁻ (x : E) in closedBall 0 a, ENNReal.ofReal (rexp (C * a ^ 2)) ∂μ

Tactic:
by
      refine setLIntegral_mono (by fun_prop) fun x hx ↦ ?_
      gcongr
      · exact mul_nonneg (logRatio_nonneg hc'_gt.le hc'_le) (by positivity)
      · simpa using hx

Goal state after:
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
⊢ AEMeasurable (fun x ↦ x.ofLp i) (multivariateGaussian μ S)

Tactic:
exact Measurable.aemeasurable <| by fun_prop

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

Goal state before:
pp: I : Finset ℝ≥0
s : ↥I
⊢ (ContinuousLinearMap.proj s) (∫ (x : ↥I → ℝ), x ∂projectiveFamily I) = 0

pp: case h
I : Finset ℝ≥0
s : ↥I
⊢ Integrable id (projectiveFamily I)

Tactic:
integral_id_projectiveFamily,

Goal state after:
I : Finset ℝ≥0
s : ↥I
⊢ (ContinuousLinearMap.proj s) 0 = 0

case h
I : Finset ℝ≥0
s : ↥I
⊢ Integrable id (projectiveFamily I)