mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.Probability.Independence.ZeroOne

Goal state before:
pp: case refine_2
α : Type u_1
Ω : Type u_2
ι : Type u_3
_mα : MeasurableSpace α
s : ι → MeasurableSpace Ω
m0 : MeasurableSpace Ω
κ : Kernel α Ω
μα : Measure α
inst✝² : SemilatticeInf ι
inst✝¹ : NoMinOrder ι
inst✝ : Nonempty ι
h_le : ∀ (n : ι), s n ≤ m0
h_indep : iIndep s κ μα
ns : ι → Set ι := Set.Ici
hnsp : ∀ (i : ι), BddBelow (ns i)
⊢ Directed (fun x1 x2 ↦ x1 ⊆ x2) ns

Tactic:
exact Antitone.directed_le fun _ _ ↦ Set.Ici_subset_Ici.2

Goal state after:
Module: Mathlib.Probability.Kernel.Category.SFinKer

Goal state before:
pp: W✝ X✝ Y✝ Z✝ : SFinKer
κ : W✝.Hom X✝
η : X✝.Hom Y✝
ξ : Y✝.Hom Z✝
⊢ { hom := ξ.hom ∘ₖ { hom := η.hom ∘ₖ κ.hom, property := ⋯ }.hom, property := ⋯ } =
    { hom := { hom := ξ.hom ∘ₖ η.hom, property := ⋯ }.hom ∘ₖ κ.hom, property := ⋯ }

Tactic:
simp [Kernel.comp_assoc]

Goal state after:
Module: Mathlib.Probability.Kernel.Proper

Goal state before:
pp: X : Type u_1
𝓑 𝓧 : MeasurableSpace X
π : Kernel X X
B : Set X
f : X → ℝ≥0∞
x₀ : X
hπ : π.IsProper
h𝓑𝓧 : 𝓑 ≤ 𝓧
hf : Measurable f
hB : MeasurableSet B
⊢ ∫⁻ (x : X), B.indicator 1 x * f x ∂π x₀ = B.indicator 1 x₀ * ∫⁻ (x : X), f x ∂π x₀

Tactic:
refine hf.ennreal_induction ?_ ?_ ?_

Goal state after:
case refine_1
X : Type u_1
𝓑 𝓧 : MeasurableSpace X
π : Kernel X X
B : Set X
f : X → ℝ≥0∞
x₀ : X
hπ : π.IsProper
h𝓑𝓧 : 𝓑 ≤ 𝓧
hf : Measurable f
hB : MeasurableSet B
⊢ ∀ (c : ℝ≥0∞) ⦃s : Set X⦄,
    MeasurableSet s →
      ∫⁻ (x : X), B.indicator 1 x * s.indicator (fun x ↦ c) x ∂π x₀ =
        B.indicator 1 x₀ * ∫⁻ (x : X), s.indicator (fun x ↦ c) x ∂π x₀

case refine_2
X : Type u_1
𝓑 𝓧 : MeasurableSpace X
π : Kernel X X
B : Set X
f : X → ℝ≥0∞
x₀ : X
hπ : π.IsProper
h𝓑𝓧 : 𝓑 ≤ 𝓧
hf : Measurable f
hB : MeasurableSet B
⊢ ∀ ⦃f g : X → ℝ≥0∞⦄,
    Disjoint (Function.support f) (Function.support g) →
      Measurable f →
        Measurable g →
          ∫⁻ (x : X), B.indicator 1 x * f x ∂π x₀ = B.indicator 1 x₀ * ∫⁻ (x : X), f x ∂π x₀ →
            ∫⁻ (x : X), B.indicator 1 x * g x ∂π x₀ = B.indicator 1 x₀ * ∫⁻ (x : X), g x ∂π x₀ →
              ∫⁻ (x : X), B.indicator 1 x * (f + g) x ∂π x₀ = B.indicator 1 x₀ * ∫⁻ (x : X), (f + g) x ∂π x₀

case refine_3
X : Type u_1
𝓑 𝓧 : MeasurableSpace X
π : Kernel X X
B : Set X
f : X → ℝ≥0∞
x₀ : X
hπ : π.IsProper
h𝓑𝓧 : 𝓑 ≤ 𝓧
hf : Measurable f
hB : MeasurableSet B
⊢ ∀ ⦃f : ℕ → X → ℝ≥0∞⦄,
    (∀ (n : ℕ), Measurable (f n)) →
      Monotone f →
        (∀ (n : ℕ), ∫⁻ (x : X), B.indicator 1 x * f n x ∂π x₀ = B.indicator 1 x₀ * ∫⁻ (x : X), f n x ∂π x₀) →
          ∫⁻ (x : X), B.indicator 1 x * (fun x ↦ ⨆ n, f n x) x ∂π x₀ =
            B.indicator 1 x₀ * ∫⁻ (x : X), (fun x ↦ ⨆ n, f n x) x ∂π x₀
Module: Mathlib.Probability.Independence.Conditional

Goal state before:
pp: Ω : Type u_1
m' mΩ : MeasurableSpace Ω
inst✝¹ : StandardBorelSpace Ω
hm' : m' ≤ mΩ
s t : Set Ω
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
⊢ CondIndepSet m' hm' s t μ ↔ CondIndep m' (generateFrom {s}) (generateFrom {t}) hm' μ

Tactic:
simp only [CondIndepSet, CondIndep, Kernel.IndepSet]

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

Goal state before:
pp: case refine_2
Ω : Type u_1
ι : Type u_2
m' mΩ : MeasurableSpace Ω
inst✝¹ : StandardBorelSpace Ω
hm' : m' ≤ mΩ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
β : ι → Type u_5
m : (x : ι) → MeasurableSpace (β x)
f : (i : ι) → Ω → β i
hf : ∀ (i : ι), Measurable (f i)
s : Finset ι
sets : ι → Set Ω
h_sets : ∀ i ∈ s, MeasurableSet (sets i)
g : (i : ι) → Set (β i) := fun i ↦ if hi : i ∈ s then Exists.choose ⋯ else Set.univ
h : μ[(⋂ i ∈ s, f i ⁻¹' g i).indicator fun ω ↦ 1 | m'] =ᵐ[μ] ∏ i ∈ s, μ[(f i ⁻¹' g i).indicator fun ω ↦ 1 | m']
⊢ μ[(⋂ i ∈ s, sets i).indicator fun ω ↦ 1 | m'] =ᵐ[μ] ∏ i ∈ s, μ[(sets i).indicator fun ω ↦ 1 | m']

Tactic:
have hg : ∀ i ∈ s, sets i = f i ⁻¹' g i := by
        intro i hi
        rw [(h_sets i hi).choose_spec.2.symm]
        simp only [g, dif_pos hi]
      convert! h with i hi i hi <;> exact hg i hi

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

Goal state before:
pp: Ω : Type u_1
β : Type u_3
β' : Type u_4
m' mΩ : MeasurableSpace Ω
inst✝¹ : StandardBorelSpace Ω
hm' : m' ≤ mΩ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
mβ : MeasurableSpace β
mβ' : MeasurableSpace β'
X : Ω → β
Y : Ω → β'
hX : Measurable X
hY : Measurable Y
s : Set β
hs : MeasurableSet s
t : Set β'
ht : MeasurableSet t
⊢ μ[(X ⁻¹' s ∩ Y ⁻¹' t).indicator fun ω ↦ 1 | m'] =ᵐ[μ]
    μ[(X ⁻¹' s).indicator fun ω ↦ 1 | m'] * μ[(Y ⁻¹' t).indicator fun ω ↦ 1 | m']

Tactic:
show (fun ω : Ω ↦ (1 : ℝ)) = 1 from rfl,

Goal state after:
Ω : Type u_1
β : Type u_3
β' : Type u_4
m' mΩ : MeasurableSpace Ω
inst✝¹ : StandardBorelSpace Ω
hm' : m' ≤ mΩ
μ : Measure Ω
inst✝ : IsFiniteMeasure μ
mβ : MeasurableSpace β
mβ' : MeasurableSpace β'
X : Ω → β
Y : Ω → β'
hX : Measurable X
hY : Measurable Y
s : Set β
hs : MeasurableSet s
t : Set β'
ht : MeasurableSet t
⊢ μ[(X ⁻¹' s ∩ Y ⁻¹' t).indicator 1 | m'] =ᵐ[μ] μ[(X ⁻¹' s).indicator 1 | m'] * μ[(Y ⁻¹' t).indicator 1 | m']
Module: Mathlib.Probability.ProbabilityMassFunction.Binomial

Goal state before:
pp: k b : ℕ
hb : k ≤ b
x : ℝ≥0
h : x ≤ 1
eq0 : k % (b + 1) = k
eq1 : 1 - ↑x = ENNReal.ofReal (1 - ↑x)
this : 1 - ↑x ≥ 0
⊢ ENNReal.ofReal (↑(b.choose k) * ↑x ^ k * (1 - ↑x) ^ (b - k)) =
    ↑x ^ (k % (b + 1)) * (1 - ↑x) ^ (↑(Fin.last b) - k % (b + 1)) * ↑(b.choose (k % (b + 1)))

Tactic:
Fin.val_last,

Goal state after:
k b : ℕ
hb : k ≤ b
x : ℝ≥0
h : x ≤ 1
eq0 : k % (b + 1) = k
eq1 : 1 - ↑x = ENNReal.ofReal (1 - ↑x)
this : 1 - ↑x ≥ 0
⊢ ENNReal.ofReal (↑(b.choose k) * ↑x ^ k * (1 - ↑x) ^ (b - k)) =
    ↑x ^ (k % (b + 1)) * (1 - ↑x) ^ (b - k % (b + 1)) * ↑(b.choose (k % (b + 1)))
Module: Mathlib.Probability.Independence.Conditional

Goal state before:
pp: Ω : Type u_1
β : Type u_3
β' : Type u_4
mΩ : MeasurableSpace Ω
inst✝⁵ : StandardBorelSpace Ω
μ : Measure Ω
inst✝⁴ : IsFiniteMeasure μ
f : Ω → β
g : Ω → β'
γ : Type u_5
mγ : MeasurableSpace γ
mβ : MeasurableSpace β
mβ' : MeasurableSpace β'
inst✝³ : StandardBorelSpace β
inst✝² : Nonempty β
inst✝¹ : StandardBorelSpace β'
inst✝ : Nonempty β'
hf : Measurable f
hg : Measurable g
k : Ω → γ
hk : Measurable k
e : γ × β' × β ≃ᵐ (γ × β') × β := MeasurableEquiv.prodAssoc.symm
h_eq :
  ⇑(Kernel.id ×ₖ condDistrib g k μ ×ₖ condDistrib f k μ) ∘ₘ Measure.map k μ =
    ⇑(Kernel.id ×ₖ Kernel.prodMkRight β' (condDistrib f k μ)) ∘ₘ Measure.map (fun a ↦ (k a, g a)) μ
h1 : Measure.map (fun x ↦ ((k x, g x), f x)) μ = Measure.map (⇑e) (Measure.map (fun a ↦ (k a, g a, f a)) μ)
h1_symm : Measure.map (fun a ↦ (k a, g a, f a)) μ = Measure.map (⇑e.symm) (Measure.map (fun x ↦ ((k x, g x), f x)) μ)
h2 :
  ⇑(Kernel.id ×ₖ condDistrib g k μ ×ₖ condDistrib f k μ) ∘ₘ Measure.map k μ =
    Measure.map (⇑e) (⇑(Kernel.id ×ₖ (condDistrib g k μ ×ₖ condDistrib f k μ)) ∘ₘ Measure.map k μ)
h2_symm :
  ⇑(Kernel.id ×ₖ (condDistrib g k μ ×ₖ condDistrib f k μ)) ∘ₘ Measure.map k μ =
    Measure.map (⇑e.symm) (⇑(Kernel.id ×ₖ condDistrib g k μ ×ₖ condDistrib f k μ) ∘ₘ Measure.map k μ)
h :
  Measure.map (⇑e) (Measure.map (fun a ↦ (k a, g a, f a)) μ) =
    Measure.map (⇑e) (⇑(Kernel.id ×ₖ (condDistrib g k μ ×ₖ condDistrib f k μ)) ∘ₘ Measure.map k μ)
⊢ Measure.map (fun ω ↦ (k ω, g ω, f ω)) μ = ⇑(Kernel.id ×ₖ (condDistrib g k μ ×ₖ condDistrib f k μ)) ∘ₘ Measure.map k μ

Tactic:
by rw [h1_symm, h1, h2_symm, h2, h]

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

Goal state before:
pp: ι : Type u_1
Ω : Type u_2
mΩ : MeasurableSpace Ω
P : Measure Ω
inst✝² : LinearOrder ι
𝓕 : Filtration ι mΩ
inst✝¹ : TopologicalSpace ι
inst✝ : OrderTopology ι
τ σ : ℕ → Ω → WithTop ι
hτ : IsLocalizingSequence 𝓕 τ P
hσ : IsLocalizingSequence 𝓕 σ P
⊢ ∀ᵐ (ω : Ω) ∂P, Tendsto (fun x ↦ (τ ⊓ σ) x ω) atTop (𝓝 ⊤)

Tactic:
filter_upwards [hτ.tendsto_top, hσ.tendsto_top] with ω hτω hσω using hτω.min hσω

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

Goal state before:
pp: Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X Y : Ω → ℝ
cX cY : ℝ≥0
hX : HasSubgaussianMGF X cX κ ν
hY : HasSubgaussianMGF Y cY κ ν
hX0 : ¬cX = 0
hY0 : ¬cY = 0
t : ℝ
⊢ Integrable (fun ω ↦ rexp (t * (X ω + Y ω))) (⇑κ ∘ₘ ν)

Tactic:
mul_add,

Goal state after:
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X Y : Ω → ℝ
cX cY : ℝ≥0
hX : HasSubgaussianMGF X cX κ ν
hY : HasSubgaussianMGF Y cY κ ν
hX0 : ¬cX = 0
hY0 : ¬cY = 0
t : ℝ
⊢ Integrable (fun ω ↦ rexp (t * X ω + t * Y ω)) (⇑κ ∘ₘ ν)
Module: Mathlib.Probability.Moments.SubGaussian

Goal state before:
pp: Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X Y : Ω → ℝ
cX cY : ℝ≥0
hX : HasSubgaussianMGF X cX κ ν
hY : HasSubgaussianMGF Y cY κ ν
hX0 : ¬cX = 0
hY0 : ¬cY = 0
p : ℝ≥0 := (NNReal.sqrt cX + NNReal.sqrt cY) / NNReal.sqrt cX
q : ℝ≥0 := (NNReal.sqrt cX + NNReal.sqrt cY) / NNReal.sqrt cY
ω' : Ω'
hmX : ∀ (t : ℝ), mgf X (κ ω') t ≤ rexp (↑cX * t ^ 2 / 2)
hmY : ∀ (t : ℝ), mgf Y (κ ω') t ≤ rexp (↑cY * t ^ 2 / 2)
hlX : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * X ω)) (↑p) (κ ω')
hlY : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * Y ω)) (↑q) (κ ω')
t : ℝ
⊢ ∫ (x : Ω), rexp (t * (X x + Y x)) ∂κ ω' ≤
    (∫ (x : Ω), rexp (t * X x) ^ ↑p ∂κ ω') ^ (1 / ↑p) * (∫ (x : Ω), rexp (t * Y x) ^ ↑q ∂κ ω') ^ (1 / ↑q)

Tactic:
mul_add,

Goal state after:
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X Y : Ω → ℝ
cX cY : ℝ≥0
hX : HasSubgaussianMGF X cX κ ν
hY : HasSubgaussianMGF Y cY κ ν
hX0 : ¬cX = 0
hY0 : ¬cY = 0
p : ℝ≥0 := (NNReal.sqrt cX + NNReal.sqrt cY) / NNReal.sqrt cX
q : ℝ≥0 := (NNReal.sqrt cX + NNReal.sqrt cY) / NNReal.sqrt cY
ω' : Ω'
hmX : ∀ (t : ℝ), mgf X (κ ω') t ≤ rexp (↑cX * t ^ 2 / 2)
hmY : ∀ (t : ℝ), mgf Y (κ ω') t ≤ rexp (↑cY * t ^ 2 / 2)
hlX : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * X ω)) (↑p) (κ ω')
hlY : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * Y ω)) (↑q) (κ ω')
t : ℝ
⊢ ∫ (x : Ω), rexp (t * X x + t * Y x) ∂κ ω' ≤
    (∫ (x : Ω), rexp (t * X x) ^ ↑p ∂κ ω') ^ (1 / ↑p) * (∫ (x : Ω), rexp (t * Y x) ^ ↑q ∂κ ω') ^ (1 / ↑q)
Module: Mathlib.Probability.Moments.SubGaussian

Goal state before:
pp: case hg_nonneg
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X Y : Ω → ℝ
cX cY : ℝ≥0
hX : HasSubgaussianMGF X cX κ ν
hY : HasSubgaussianMGF Y cY κ ν
hX0 : ¬cX = 0
hY0 : ¬cY = 0
p : ℝ≥0 := ⋯
q : ℝ≥0 := ⋯
ω' : Ω'
hmX : ∀ (t : ℝ), mgf X (κ ω') t ≤ rexp (↑cX * t ^ 2 / 2)
hmY : ∀ (t : ℝ), mgf Y (κ ω') t ≤ rexp (↑cY * t ^ 2 / 2)
hlX : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * X ω)) (↑p) (κ ω')
hlY : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * Y ω)) (↑q) (κ ω')
t : ℝ
⊢ 0 ≤ᵐ[κ ω'] fun a ↦ rexp (t * Y a)

pp: case hf
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X Y : Ω → ℝ
cX cY : ℝ≥0
hX : HasSubgaussianMGF X cX κ ν
hY : HasSubgaussianMGF Y cY κ ν
hX0 : ¬cX = 0
hY0 : ¬cY = 0
p : ℝ≥0 := ⋯
q : ℝ≥0 := ⋯
ω' : Ω'
hmX : ∀ (t : ℝ), mgf X (κ ω') t ≤ rexp (↑cX * t ^ 2 / 2)
hmY : ∀ (t : ℝ), mgf Y (κ ω') t ≤ rexp (↑cY * t ^ 2 / 2)
hlX : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * X ω)) (↑p) (κ ω')
hlY : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * Y ω)) (↑q) (κ ω')
t : ℝ
⊢ MemLp (fun a ↦ rexp (t * X a)) (ENNReal.ofReal ↑p) (κ ω')

pp: case hg
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X Y : Ω → ℝ
cX cY : ℝ≥0
hX : HasSubgaussianMGF X cX κ ν
hY : HasSubgaussianMGF Y cY κ ν
hX0 : ¬cX = 0
hY0 : ¬cY = 0
p : ℝ≥0 := ⋯
q : ℝ≥0 := ⋯
ω' : Ω'
hmX : ∀ (t : ℝ), mgf X (κ ω') t ≤ rexp (↑cX * t ^ 2 / 2)
hmY : ∀ (t : ℝ), mgf Y (κ ω') t ≤ rexp (↑cY * t ^ 2 / 2)
hlX : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * X ω)) (↑p) (κ ω')
hlY : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * Y ω)) (↑q) (κ ω')
t : ℝ
⊢ MemLp (fun a ↦ rexp (t * Y a)) (ENNReal.ofReal ↑q) (κ ω')

Tactic:
· exact ae_of_all _ fun _ ↦ exp_nonneg _

Goal state after:
case hf
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X Y : Ω → ℝ
cX cY : ℝ≥0
hX : HasSubgaussianMGF X cX κ ν
hY : HasSubgaussianMGF Y cY κ ν
hX0 : ¬cX = 0
hY0 : ¬cY = 0
p : ℝ≥0 := ⋯
q : ℝ≥0 := ⋯
ω' : Ω'
hmX : ∀ (t : ℝ), mgf X (κ ω') t ≤ rexp (↑cX * t ^ 2 / 2)
hmY : ∀ (t : ℝ), mgf Y (κ ω') t ≤ rexp (↑cY * t ^ 2 / 2)
hlX : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * X ω)) (↑p) (κ ω')
hlY : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * Y ω)) (↑q) (κ ω')
t : ℝ
⊢ MemLp (fun a ↦ rexp (t * X a)) (ENNReal.ofReal ↑p) (κ ω')

case hg
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X Y : Ω → ℝ
cX cY : ℝ≥0
hX : HasSubgaussianMGF X cX κ ν
hY : HasSubgaussianMGF Y cY κ ν
hX0 : ¬cX = 0
hY0 : ¬cY = 0
p : ℝ≥0 := ⋯
q : ℝ≥0 := ⋯
ω' : Ω'
hmX : ∀ (t : ℝ), mgf X (κ ω') t ≤ rexp (↑cX * t ^ 2 / 2)
hmY : ∀ (t : ℝ), mgf Y (κ ω') t ≤ rexp (↑cY * t ^ 2 / 2)
hlX : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * X ω)) (↑p) (κ ω')
hlY : ∀ (t : ℝ), MemLp (fun ω ↦ rexp (t * Y ω)) (↑q) (κ ω')
t : ℝ
⊢ MemLp (fun a ↦ rexp (t * Y a)) (ENNReal.ofReal ↑q) (κ ω')
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: case refine_3
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : Integrable f μ
⊢ Integrable (fun x ↦ |f x|) μ

pp: case refine_4
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : Integrable f μ
⊢ ∀ᵐ (a : α) ∂μ, Tendsto (fun n ↦ truncation f n a) atTop (𝓝 (f a))

Tactic:
· exact hf.abs

Goal state after:
case refine_4
α : Type u_1
m : MeasurableSpace α
μ : Measure α
f : α → ℝ
hf : Integrable f μ
⊢ ∀ᵐ (a : α) ∂μ, Tendsto (fun n ↦ truncation f n a) atTop (𝓝 (f a))
Module: Mathlib.Probability.Moments.SubGaussian

Goal state before:
pp: case pos.inr
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
inst✝¹ : SFinite ν
η : Kernel (Ω' × Ω) Ω''
inst✝ : IsZeroOrMarkovKernel η
hX : HasSubgaussianMGF X c κ ν
hY : HasSubgaussianMGF Y cY η (ν ⊗ₘ κ)
t : ℝ
hκ : IsSFiniteKernel κ
hη : IsMarkovKernel η
⊢ Integrable (fun ω ↦ rexp (t * (X ω.1 + Y ω.2))) (⇑(κ ⊗ₖ η) ∘ₘ ν)

Tactic:
mul_add,

Goal state after:
case pos.inr
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
inst✝¹ : SFinite ν
η : Kernel (Ω' × Ω) Ω''
inst✝ : IsZeroOrMarkovKernel η
hX : HasSubgaussianMGF X c κ ν
hY : HasSubgaussianMGF Y cY η (ν ⊗ₘ κ)
t : ℝ
hκ : IsSFiniteKernel κ
hη : IsMarkovKernel η
⊢ Integrable (fun ω ↦ rexp (t * X ω.1 + t * Y ω.2)) (⇑(κ ⊗ₖ η) ∘ₘ ν)
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: case h
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K N : ℕ
hKN : K ≤ N
ρ : Measure ℝ := ⋯
this : IsProbabilityMeasure ρ
i : ℕ
a✝ : i ∈ range N
I : ↑i ≤ ↑(i + 1)
⊢ ∀ x ∈ Set.Ioc ↑i ↑(i + 1), (↑i + 1) * 1 ≤ x + 1

Tactic:
intro x hx
          simp only [Nat.cast_add, Nat.cast_one, Set.mem_Ioc] at hx
          simp [hx.1.le]

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

Goal state before:
pp: Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
inst✝¹ : SFinite ν
η : Kernel (Ω' × Ω) Ω''
inst✝ : IsZeroOrMarkovKernel η
hX : HasSubgaussianMGF X c κ ν
hY : HasSubgaussianMGF Y cY η (ν ⊗ₘ κ)
hκ : IsSFiniteKernel κ
q : ℚ
ω' : Ω'
hX_mgf : ∀ (t : ℝ), mgf X (κ ω') t ≤ rexp (↑c * t ^ 2 / 2)
hX_int : Integrable (fun y ↦ rexp (↑q * X y)) (κ ω')
hY_mgf : ∀ᵐ (b : Ω) ∂κ ω', ∀ (t : ℝ), mgf Y (η (ω', b)) t ≤ rexp (↑cY * t ^ 2 / 2)
h_int_mul : Integrable (fun ω ↦ rexp (↑q * (X ω.1 + Y ω.2))) ((κ ⊗ₖ η) ω')
⊢ ∫ (x : Ω × Ω''), rexp (↑q * (X x.1 + Y x.2)) ∂(κ ⊗ₖ η) ω' =
    ∫ (x : Ω), rexp (↑q * X x) * ∫ (y : Ω''), rexp (↑q * Y y) ∂η (ω', x) ∂κ ω'

Tactic:
mul_add,

Goal state after:
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
inst✝¹ : SFinite ν
η : Kernel (Ω' × Ω) Ω''
inst✝ : IsZeroOrMarkovKernel η
hX : HasSubgaussianMGF X c κ ν
hY : HasSubgaussianMGF Y cY η (ν ⊗ₘ κ)
hκ : IsSFiniteKernel κ
q : ℚ
ω' : Ω'
hX_mgf : ∀ (t : ℝ), mgf X (κ ω') t ≤ rexp (↑c * t ^ 2 / 2)
hX_int : Integrable (fun y ↦ rexp (↑q * X y)) (κ ω')
hY_mgf : ∀ᵐ (b : Ω) ∂κ ω', ∀ (t : ℝ), mgf Y (η (ω', b)) t ≤ rexp (↑cY * t ^ 2 / 2)
h_int_mul : Integrable (fun ω ↦ rexp (↑q * X ω.1 + ↑q * Y ω.2)) ((κ ⊗ₖ η) ω')
⊢ ∫ (x : Ω × Ω''), rexp (↑q * X x.1 + ↑q * Y x.2) ∂(κ ⊗ₖ η) ω' =
    ∫ (x : Ω), rexp (↑q * X x) * ∫ (y : Ω''), rexp (↑q * Y y) ∂η (ω', x) ∂κ ω'
Module: Mathlib.Probability.Moments.SubGaussian

Goal state before:
pp: Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
inst✝¹ : SFinite ν
η : Kernel (Ω' × Ω) Ω''
inst✝ : IsZeroOrMarkovKernel η
hX : HasSubgaussianMGF X c κ ν
hY : HasSubgaussianMGF Y cY η (ν ⊗ₘ κ)
hκ : IsSFiniteKernel κ
q : ℚ
ω' : Ω'
hX_mgf : ∀ (t : ℝ), mgf X (κ ω') t ≤ rexp (↑c * t ^ 2 / 2)
hX_int : Integrable (fun y ↦ rexp (↑q * X y)) (κ ω')
hY_mgf : ∀ᵐ (b : Ω) ∂κ ω', ∀ (t : ℝ), mgf Y (η (ω', b)) t ≤ rexp (↑cY * t ^ 2 / 2)
h_int_mul : Integrable (fun ω ↦ rexp (↑q * (X ω.1 + Y ω.2))) ((κ ⊗ₖ η) ω')
⊢ mgf (fun p ↦ X p.1 + Y p.2) ((κ ⊗ₖ η) ω') ↑q =
    ∫ (x : Ω), rexp (↑q * X x) * ∫ (y : Ω''), rexp (↑q * Y y) ∂η (ω', x) ∂κ ω'

Tactic:
simp_rw [mgf, mul_add, exp_add] at h_int_mul ⊢

Goal state after:
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
X : Ω → ℝ
c : ℝ≥0
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
inst✝¹ : SFinite ν
η : Kernel (Ω' × Ω) Ω''
inst✝ : IsZeroOrMarkovKernel η
hX : HasSubgaussianMGF X c κ ν
hY : HasSubgaussianMGF Y cY η (ν ⊗ₘ κ)
hκ : IsSFiniteKernel κ
q : ℚ
ω' : Ω'
hX_mgf : ∀ (t : ℝ), mgf X (κ ω') t ≤ rexp (↑c * t ^ 2 / 2)
hX_int : Integrable (fun y ↦ rexp (↑q * X y)) (κ ω')
hY_mgf : ∀ᵐ (b : Ω) ∂κ ω', ∀ (t : ℝ), mgf Y (η (ω', b)) t ≤ rexp (↑cY * t ^ 2 / 2)
h_int_mul : Integrable (fun ω ↦ rexp (↑q * X ω.1) * rexp (↑q * Y ω.2)) ((κ ⊗ₖ η) ω')
⊢ ∫ (x : Ω × Ω''), rexp (↑q * X x.1) * rexp (↑q * Y x.2) ∂(κ ⊗ₖ η) ω' =
    ∫ (x : Ω), rexp (↑q * X x) * ∫ (y : Ω''), rexp (↑q * Y y) ∂η (ω', x) ∂κ ω'
Module: Mathlib.Probability.Moments.SubGaussian

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
X Y : Ω → ℝ
cX cY : ℝ≥0
hX : HasSubgaussianMGF X cX μ
hY : HasSubgaussianMGF Y cY μ
hindep : X ⟂ᵢ[μ] Y
t : ℝ
⊢ Integrable (fun ω ↦ rexp (t * (X ω + Y ω))) μ

Tactic:
mul_add,

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
X Y : Ω → ℝ
cX cY : ℝ≥0
hX : HasSubgaussianMGF X cX μ
hY : HasSubgaussianMGF Y cY μ
hindep : X ⟂ᵢ[μ] Y
t : ℝ
⊢ Integrable (fun ω ↦ rexp (t * X ω + t * Y ω)) μ
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : ℕ → Ω → ℝ
hint : Integrable (X 0) ℙ
hindep : Pairwise ((fun f g ↦ f ⟂ᵢ g) on X)
hident : ∀ (i : ℕ), IdentDistrib (X i) (X 0) ℙ ℙ
hnonneg : ∀ (i : ℕ) (ω : Ω), 0 ≤ X i ω
c : ℝ
c_one : 1 < c
ε : ℝ
εpos : 0 < ε
c_pos : 0 < c
hX : ∀ (i : ℕ), AEStronglyMeasurable (X i) ℙ
A : ∀ (i : ℝ), StronglyMeasurable ((Set.Ioc (-i) i).indicator id)
Y : ℕ → Ω → ℝ := fun n ↦ truncation (X n) ↑n
S : ℕ → Ω → ℝ := fun n ↦ ∑ i ∈ range n, Y i
hS : S = fun n ↦ ∑ i ∈ range n, Y i
u : ℕ → ℕ := fun n ↦ ⌊c ^ n⌋₊
u_mono : Monotone u
I1 : ∀ (K : ℕ), ∑ j ∈ range K, (↑j ^ 2)⁻¹ * Var[Y j; ℙ] ≤ 2 * ∫ (a : Ω), X 0 a
C : ℝ := c ^ 5 * (c - 1)⁻¹ ^ 3 * (2 * ∫ (a : Ω), X 0 a)
I2 : ∀ (N : ℕ), ∑ i ∈ range N, (↑(u i) ^ 2)⁻¹ * Var[S (u i); ℙ] ≤ C
N i : ℕ
x✝ : i ∈ range N
⊢ 0 ≤ Var[S (u i); ℙ] / (↑(u i) * ε) ^ 2

Tactic:
exact div_nonneg (variance_nonneg _ _) (sq_nonneg _)

Goal state after:
Module: Mathlib.RepresentationTheory.Character

Goal state before:
pp: G : Type u_1
k : Type u_2
V : Type u_3
inst✝³ : Monoid G
inst✝² : Field k
inst✝¹ : AddCommGroup V
inst✝ : Module k V
ρ : Representation k G V
g h : G
⊢ ρ.character (h * g) = ρ.character (g * h)

Tactic:
simp only [trace_mul_comm, character, map_mul]

Goal state after:
Module: Mathlib.RepresentationTheory.Character

Goal state before:
pp: k : Type u
inst✝¹ : Field k
G : Type v
inst✝ : Monoid G
V : FDRep k G
g h : G
⊢ V.character (h * g) = V.character (g * h)

Tactic:
simp only [trace_mul_comm, character, map_mul]

Goal state after:
Module: Mathlib.RepresentationTheory.Continuous.Basic

Goal state before:
pp: R : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝¹⁸ : Monoid G
inst✝¹⁷ : Ring R
inst✝¹⁶ : AddCommGroup V
inst✝¹⁵ : TopologicalSpace V
inst✝¹⁴ : IsTopologicalAddGroup V
inst✝¹³ : Module R V
inst✝¹² : AddCommGroup W
inst✝¹¹ : TopologicalSpace W
inst✝¹⁰ : IsTopologicalAddGroup W
inst✝⁹ : Module R W
inst✝⁸ : AddCommGroup U
inst✝⁷ : Module R U
inst✝⁶ : TopologicalSpace U
inst✝⁵ : IsTopologicalAddGroup U
π₁ : ContRepresentation R G V
π₂ : ContRepresentation R G W
π₃ : ContRepresentation R G U
S : Type u_6
inst✝⁴ : Monoid S
inst✝³ : DistribMulAction S W
inst✝² : SMulCommClass R S W
inst✝¹ : ContinuousConstSMul S W
inst✝ : LinearMap.CompatibleSMul W W S R
x✝³ x✝² : S
x✝¹ : π₁ →ⁱL π₂
x✝ : V
⊢ ((x✝³ * x✝²) • x✝¹).toContinuousLinearMap x✝ = (x✝³ • x✝² • x✝¹).toContinuousLinearMap x✝

Tactic:
simp [mul_smul]

Goal state after:
Module: Mathlib.RepresentationTheory.FinGroupCharZero

Goal state before:
pp: case mpr
k : Type u
inst✝⁴ : Field k
G : Type u
inst✝³ : Group G
inst✝² : IsAlgClosed k
inst✝¹ : CharZero k
inst✝ : Fintype G
V : FDRep k G
this : Invertible ↑(Nat.card G)
h : ∑ g, V.character g * V.character g⁻¹ = ↑(Nat.card G)
eq : 1 = ↑(Module.finrank k (V ⟶ V))
⊢ Module.finrank k (V ⟶ V) = 1

Tactic:
← Nat.cast_inj (R := k),

Goal state after:
case mpr
k : Type u
inst✝⁴ : Field k
G : Type u
inst✝³ : Group G
inst✝² : IsAlgClosed k
inst✝¹ : CharZero k
inst✝ : Fintype G
V : FDRep k G
this : Invertible ↑(Nat.card G)
h : ∑ g, V.character g * V.character g⁻¹ = ↑(Nat.card G)
eq : 1 = ↑(Module.finrank k (V ⟶ V))
⊢ ↑(Module.finrank k (V ⟶ V)) = ↑1
Module: Mathlib.RepresentationTheory.Homological.ContCohomology.Basic

Goal state before:
pp: k : Type u_1
G : Type u_2
inst✝⁴ : Ring k
inst✝³ : Group G
inst✝² : TopologicalSpace k
inst✝¹ : TopologicalSpace G
inst✝ : IsTopologicalGroup G
X : TopRep k G
i : ℕ
⊢ TopModuleCat.ofHom (Hom.hom (CochainComplex.of.d X.resolution'X X.resolution'd i (i + 1))).mapInvariants =
    TopModuleCat.ofHom (Hom.hom (X.d (i + 1))).mapInvariants

Tactic:
← resolution'd_eq,

Goal state after:
k : Type u_1
G : Type u_2
inst✝⁴ : Ring k
inst✝³ : Group G
inst✝² : TopologicalSpace k
inst✝¹ : TopologicalSpace G
inst✝ : IsTopologicalGroup G
X : TopRep k G
i : ℕ
⊢ TopModuleCat.ofHom (Hom.hom (CochainComplex.of.d X.resolution'X X.resolution'd i (i + 1))).mapInvariants =
    TopModuleCat.ofHom (Hom.hom (X.resolution'd i)).mapInvariants
Module: Mathlib.RepresentationTheory.Homological.ContCohomology.LowDegree

Goal state before:
pp: k : Type u_1
G : Type u_2
inst✝⁴ : Ring k
inst✝³ : Group G
inst✝² : TopologicalSpace k
inst✝¹ : TopologicalSpace G
inst✝ : IsTopologicalGroup G
X : TopRep k G
x : ↑X
h : ContinuousMap.const G x ∈ (X.resolution'.X 0).ρ.invariants
⊢ ↑((TopModuleCat.Hom.hom (X.homogeneousCochains.d 0 1)) ⟨ContinuousMap.const G x, h⟩) = ↑0

Tactic:
homogeneousCochains.d_apply

Goal state after:
k : Type u_1
G : Type u_2
inst✝⁴ : Ring k
inst✝³ : Group G
inst✝² : TopologicalSpace k
inst✝¹ : TopologicalSpace G
inst✝ : IsTopologicalGroup G
X : TopRep k G
x : ↑X
h : ContinuousMap.const G x ∈ (X.resolution'.X 0).ρ.invariants
⊢ (Hom.hom (X.d (0 + 1))) ↑⟨ContinuousMap.const G x, h⟩ = ↑0