mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.Probability.Kernel.IonescuTulcea.Traj

Goal state before:
pp: case cylinder
X : ℕ → Type u_1
inst✝¹ : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a : ℕ
t : Set ((n : ℕ) → X n)
ht : t ∈ measurableCylinders X
N : ℕ
S : Set ((i : ↥(Iic N)) → X ↑i)
mS : MeasurableSet S
t_eq : t = cylinder (Iic N) S
⊢ Measurable fun x₀ ↦ (trajFun κ a x₀) t

Tactic:
simp_rw [trajFun, AddContent.measure_eq _ _ generateFrom_measurableCylinders.symm _ ht,
      trajContent, projectiveFamilyContent_congr _ t t_eq mS, inducedFamily]

Goal state after:
case cylinder
X : ℕ → Type u_1
inst✝¹ : (n : ℕ) → MeasurableSpace (X n)
κ : (n : ℕ) → Kernel ((i : ↥(Iic n)) → X ↑i) (X (n + 1))
inst✝ : ∀ (n : ℕ), IsMarkovKernel (κ n)
a : ℕ
t : Set ((n : ℕ) → X n)
ht : t ∈ measurableCylinders X
N : ℕ
S : Set ((i : ↥(Iic N)) → X ↑i)
mS : MeasurableSet S
t_eq : t = cylinder (Iic N) S
⊢ Measurable fun x₀ ↦ (Measure.map (restrict₂ ⋯) ((partialTraj κ a ((Iic N).sup id)) x₀)) S
Module: Mathlib.Probability.Kernel.Deterministic

Goal state before:
pp: case inr
α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
γ : Type u_3
inst✝³ : MeasurableSpace γ
κ : Kernel α β
η : Kernel β γ
inst✝² : IsMarkovKernel κ
inst✝¹ : IsMarkovKernel η
inst✝ : IsDeterministic (η ∘ₖ κ)
a : α
s : Set γ
t : Set β
hs : MeasurableSet s
ht : MeasurableSet t
this : ∀ (b : β), (η b) s * t.indicator 1 b = t.indicator (fun b ↦ (η b) s) b
h₁ : ((η ∘ₖ κ) a) s = 1
integral_le_kernel : ∫⁻ (b : β) in t, (η b) s ∂κ a ≤ (κ a) t
⊢ ∫⁻ (a : β) in t, (η a) s ∂κ a = (κ a) t

Tactic:
refine le_antisymm integral_le_kernel <| tsub_eq_zero_iff_le.mp ?_

Goal state after:
case inr
α : Type u_1
β : Type u_2
mα : MeasurableSpace α
mβ : MeasurableSpace β
γ : Type u_3
inst✝³ : MeasurableSpace γ
κ : Kernel α β
η : Kernel β γ
inst✝² : IsMarkovKernel κ
inst✝¹ : IsMarkovKernel η
inst✝ : IsDeterministic (η ∘ₖ κ)
a : α
s : Set γ
t : Set β
hs : MeasurableSet s
ht : MeasurableSet t
this : ∀ (b : β), (η b) s * t.indicator 1 b = t.indicator (fun b ↦ (η b) s) b
h₁ : ((η ∘ₖ κ) a) s = 1
integral_le_kernel : ∫⁻ (b : β) in t, (η b) s ∂κ a ≤ (κ a) t
⊢ (κ a) t - ∫⁻ (a : β) in t, (η a) s ∂κ a = 0
Module: Mathlib.Probability.ProductMeasure

Goal state before:
pp: ι : Type u_1
X : ι → Type u_2
mX : (i : ι) → MeasurableSpace (X i)
μ : (i : ι) → Measure (X i)
hμ : ∀ (i : ι), IsProbabilityMeasure (μ i)
s : Finset ι
S : Set ((i : ↥s) → X ↑i)
mS : MeasurableSet S
⊢ (infinitePi μ) (cylinder s S) = (Measure.pi fun i ↦ μ ↑i) S

Tactic:
rw [cylinder, ← Measure.map_apply (measurable_restrict _) mS, infinitePi_map_restrict]

Goal state after:
Module: Mathlib.Probability.ProductMeasure

Goal state before:
pp: ι : Type u_6
κ : Type u_7
X : Type u_8
mX : MeasurableSpace X
μ : ι → κ → Measure X
hμ : ∀ (i : ι) (j : κ), IsProbabilityMeasure (μ i j)
⊢ map (⇑(MeasurableEquiv.curry ι κ X).symm) (infinitePi fun i ↦ infinitePi fun j ↦ μ i j) = infinitePi fun p ↦ μ p.1 p.2

Tactic:
← (MeasurableEquiv.piCongrLeft (fun _ ↦ X)
    (Equiv.sigmaEquivProd ι κ).symm).map_measurableEquiv_injective.eq_iff,

Goal state after:
ι : Type u_6
κ : Type u_7
X : Type u_8
mX : MeasurableSpace X
μ : ι → κ → Measure X
hμ : ∀ (i : ι) (j : κ), IsProbabilityMeasure (μ i j)
⊢ map (⇑(piCongrLeft (fun x ↦ X) (Equiv.sigmaEquivProd ι κ).symm))
      (map (⇑(MeasurableEquiv.curry ι κ X).symm) (infinitePi fun i ↦ infinitePi fun j ↦ μ i j)) =
    map (⇑(piCongrLeft (fun x ↦ X) (Equiv.sigmaEquivProd ι κ).symm)) (infinitePi fun p ↦ μ p.1 p.2)
Module: Mathlib.Probability.Distributions.Beta

Goal state before:
pp: α β : ℝ
hα : 0 < α
hβ : 0 < β
⊢ beta α β = (Gamma ↑α * Gamma ↑β / Gamma (↑α + ↑β)).re

Tactic:
beta,

Goal state after:
α β : ℝ
hα : 0 < α
hβ : 0 < β
⊢ Real.Gamma α * Real.Gamma β / Real.Gamma (α + β) = (Gamma ↑α * Gamma ↑β / Gamma (↑α + ↑β)).re
Module: Mathlib.Probability.ProbabilityMassFunction.Monad

Goal state before:
pp: α : Type u_1
a : α
inst✝¹ : MeasurableSpace α
inst✝ : Countable α
h : MeasurableSingletonClass α
⊢ (Measure.dirac a).toPMF = pure a

Tactic:
rw [toPMF_eq_iff_toMeasure_eq, toMeasure_pure]

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

Goal state before:
pp: r₁ r₂ : ℝ≥0
⊢ Measure.map ⇑(Nat.castAddMonoidHom ℝ) Po(r₁) ∗ Measure.map ⇑(Nat.castAddMonoidHom ℝ) Po(r₂) =
    Measure.map ⇑(Nat.castAddMonoidHom ℝ) Po(r₁ + r₂)

Tactic:
exact map_cast_poissonMeasure_conv_real _ _

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

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
inst✝ : IsZeroOrProbabilityMeasure μ
hXmeas : AEMeasurable X μ
hX : ∀ᵐ (ω : Ω) ∂μ, X ω = 0 ∨ X ω = 1
⊢ Var[X; μ] = μ.real {ω | X ω = 0} * μ.real {ω | X ω = 1}

Tactic:
obtain rfl | hμ := eq_zero_or_isProbabilityMeasure μ

Goal state after:
case inl
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
inst✝ : IsZeroOrProbabilityMeasure 0
hXmeas : AEMeasurable X 0
hX : ∀ᵐ (ω : Ω) ∂0, X ω = 0 ∨ X ω = 1
⊢ Var[X; 0] = Measure.real 0 {ω | X ω = 0} * Measure.real 0 {ω | X ω = 1}

case inr
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
inst✝ : IsZeroOrProbabilityMeasure μ
hXmeas : AEMeasurable X μ
hX : ∀ᵐ (ω : Ω) ∂μ, X ω = 0 ∨ X ω = 1
hμ : IsProbabilityMeasure μ
⊢ Var[X; μ] = μ.real {ω | X ω = 0} * μ.real {ω | X ω = 1}
Module: Mathlib.Probability.Kernel.Condexp

Goal state before:
pp: Ω : Type u_1
F : Type u_2
m mΩ : MeasurableSpace Ω
inst✝³ : StandardBorelSpace Ω
μ : Measure Ω
inst✝² : IsFiniteMeasure μ
inst✝¹ : NormedAddCommGroup F
f : Ω → F
inst✝ : NormedSpace ℝ F
hf : StronglyMeasurable f
a✝ : Nontrivial Ω
⊢ StronglyMeasurable fun ω ↦ ∫ (y : Ω), f y ∂(condDistrib id id μ) (id ω)

Tactic:
exact (hf.comp_measurable measurable_snd).integral_condDistrib

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

Goal state before:
pp: case inr.h_indep
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
β : Type u_4
p : Set ι → Prop
f : Filter ι
ns : β → Set ι
h_le : ∀ (n : ι), s n ≤ m0
hf : ∀ (t : Set ι), p t → tᶜ ∈ f
hns : Directed (fun x1 x2 ↦ x1 ⊆ x2) ns
hnsp : ∀ (a : β), p (ns a)
hμ : μα ≠ 0
η : Kernel α Ω
η_eq : ⇑κ =ᵐ[μα] ⇑η
hη : IsMarkovKernel η
h_indep : iIndep s η μα
⊢ ∀ (i : β), Indep (⨆ n ∈ ns i, s n) (limsup s f) η μα

Tactic:
exact fun a => indep_biSup_limsup h_le h_indep hf (hnsp a)

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

Goal state before:
pp: 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∞
A : Set X
hA : MeasurableSet A
⊢ ∫⁻ (x : X), c * (B.indicator 1 x * A.indicator 1 x) ∂π x₀ = B.indicator 1 x₀ * ∫⁻ (x : X), c * A.indicator 1 x ∂π x₀

Tactic:
rw [lintegral_const_mul, lintegral_const_mul, hπ.lintegral_indicator_mul_indicator h𝓑𝓧 hA hB,
      mul_left_comm] <;> measurability

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)
h :
  ∀ (S : Finset ι) {sets : (i : ι) → Set (β i)},
    (∀ i ∈ S, MeasurableSet (sets i)) →
      μ[(⋂ i ∈ S, f i ⁻¹' sets i).indicator fun ω ↦ 1 | m'] =ᵐ[μ] ∏ i ∈ S, μ[(f i ⁻¹' sets i).indicator fun ω ↦ 1 | m']
s : Finset ι
sets : ι → Set Ω
h_sets : ∀ i ∈ s, MeasurableSet (sets i)
⊢ μ[(⋂ i ∈ s, sets i).indicator fun ω ↦ 1 | m'] =ᵐ[μ] ∏ i ∈ s, μ[(sets i).indicator fun ω ↦ 1 | m']

Tactic:
let g := fun i ↦ if hi : i ∈ s then (h_sets i hi).choose else Set.univ
    specialize h s (sets := g) (fun i hi ↦ ?_)
    · simp only [g, dif_pos hi]
      exact (h_sets i hi).choose_spec.1
    · 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).indicator 1 * (Y ⁻¹' t).indicator 1 | m'] =ᵐ[μ] (X ⁻¹' s).indicator 1 * μ[(Y ⁻¹' t).indicator 1 | m']

Tactic:
by
    refine condExp_stronglyMeasurable_mul_of_bound hm' (stronglyMeasurable_const.indicator (hX hs))
      ((integrable_indicator_iff (hY ht)).2 integrableOn_const) 1 (ae_of_all μ fun ω ↦ ?_)
    rw [Set.indicator]
    split_ifs with h <;> simp

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

Goal state before:
pp: Ω : Type u_1
E : Type u_2
m : MeasurableSpace Ω
μ : Measure Ω
inst✝⁹ : NormedAddCommGroup E
inst✝⁸ : NormedSpace ℝ E
inst✝⁷ : CompleteSpace E
ι : Type u_3
inst✝⁶ : LinearOrder ι
inst✝⁵ : TopologicalSpace ι
inst✝⁴ : OrderTopology ι
inst✝³ : FirstCountableTopology ι
ℱ : Filtration ι m
inst✝² : SigmaFiniteFiltration μ ℱ
τ σ : Ω → WithTop ι
f : ι → Ω → E
n : ι
inst✝¹ : Nonempty ι
h : Martingale f ℱ μ
hτ : IsStoppingTime ℱ τ
hσ : IsStoppingTime ℱ σ
hσ_le_τ : σ ≤ τ
hτ_le : ∀ (x : Ω), τ x ≤ ↑n
hτ_countable_range : (Set.range τ).Countable
hσ_countable_range : (Set.range σ).Countable
inst✝ : SigmaFinite (μ.trim ⋯)
this✝ : SigmaFinite (μ.trim ⋯)
this : μ[stoppedValue f τ | hσ.measurableSpace] =ᵐ[μ] μ[μ[f n | hτ.measurableSpace] | hσ.measurableSpace]
⊢ stoppedValue f σ =ᵐ[μ] μ[stoppedValue f τ | hσ.measurableSpace]

Tactic:
refine (Filter.EventuallyEq.trans ?_
    (condExp_condExp_of_le ?_ (hτ.measurableSpace_le_of_le hτ_le)).symm).trans this.symm

Goal state after:
case refine_1
Ω : Type u_1
E : Type u_2
m : MeasurableSpace Ω
μ : Measure Ω
inst✝⁹ : NormedAddCommGroup E
inst✝⁸ : NormedSpace ℝ E
inst✝⁷ : CompleteSpace E
ι : Type u_3
inst✝⁶ : LinearOrder ι
inst✝⁵ : TopologicalSpace ι
inst✝⁴ : OrderTopology ι
inst✝³ : FirstCountableTopology ι
ℱ : Filtration ι m
inst✝² : SigmaFiniteFiltration μ ℱ
τ σ : Ω → WithTop ι
f : ι → Ω → E
n : ι
inst✝¹ : Nonempty ι
h : Martingale f ℱ μ
hτ : IsStoppingTime ℱ τ
hσ : IsStoppingTime ℱ σ
hσ_le_τ : σ ≤ τ
hτ_le : ∀ (x : Ω), τ x ≤ ↑n
hτ_countable_range : (Set.range τ).Countable
hσ_countable_range : (Set.range σ).Countable
inst✝ : SigmaFinite (μ.trim ⋯)
this✝ : SigmaFinite (μ.trim ⋯)
this : μ[stoppedValue f τ | hσ.measurableSpace] =ᵐ[μ] μ[μ[f n | hτ.measurableSpace] | hσ.measurableSpace]
⊢ stoppedValue f σ =ᵐ[μ] μ[f n | hσ.measurableSpace]

case refine_2
Ω : Type u_1
E : Type u_2
m : MeasurableSpace Ω
μ : Measure Ω
inst✝⁹ : NormedAddCommGroup E
inst✝⁸ : NormedSpace ℝ E
inst✝⁷ : CompleteSpace E
ι : Type u_3
inst✝⁶ : LinearOrder ι
inst✝⁵ : TopologicalSpace ι
inst✝⁴ : OrderTopology ι
inst✝³ : FirstCountableTopology ι
ℱ : Filtration ι m
inst✝² : SigmaFiniteFiltration μ ℱ
τ σ : Ω → WithTop ι
f : ι → Ω → E
n : ι
inst✝¹ : Nonempty ι
h : Martingale f ℱ μ
hτ : IsStoppingTime ℱ τ
hσ : IsStoppingTime ℱ σ
hσ_le_τ : σ ≤ τ
hτ_le : ∀ (x : Ω), τ x ≤ ↑n
hτ_countable_range : (Set.range τ).Countable
hσ_countable_range : (Set.range σ).Countable
inst✝ : SigmaFinite (μ.trim ⋯)
this✝ : SigmaFinite (μ.trim ⋯)
this : μ[stoppedValue f τ | hσ.measurableSpace] =ᵐ[μ] μ[μ[f n | hτ.measurableSpace] | hσ.measurableSpace]
⊢ hσ.measurableSpace ≤ hτ.measurableSpace
Module: Mathlib.Probability.Independence.BoundedContinuousFunction

Goal state before:
pp: Ω : Type u_1
S : Type u_2
mΩ : MeasurableSpace Ω
P : Measure Ω
E : S → Type u_4
inst✝⁵ : (s : S) → TopologicalSpace (E s)
inst✝⁴ : (s : S) → MeasurableSpace (E s)
inst✝³ : ∀ (s : S), BorelSpace (E s)
inst✝² : ∀ (s : S), HasOuterApproxClosed (E s)
X : (s : S) → Ω → E s
inst✝¹ : IsProbabilityMeasure P
inst✝ : Finite S
A : Set Ω
mA : NullMeasurableSet A P
mX : ∀ (s : S), AEMeasurable (X s) P
h : ∀ (f : ((s : S) → E s) →ᵇ ℝ), ∫ (ω : Ω) in A, f fun x ↦ X x ω ∂P = P.real A * ∫ (ω : Ω), f fun x ↦ X x ω ∂P
⊢ A.indicator 1 ⟂ᵢ[P] fun ω s ↦ X s ω

Tactic:
have := Fintype.ofFinite S
  refine indicator_indepFun_pi_of_prod_bcf mA mX fun f ↦ ?_
  convert! h (∏ s, (f s).compContinuous ⟨Function.eval s, by fun_prop⟩) <;> simp

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
h : HasSubgaussianMGF X c κ ν
⊢ ∀ᵐ (ω' : Ω') ∂ν, IsFiniteMeasure (κ ω')

Tactic:
by
  filter_upwards [h.ae_integrable_exp_mul 0, h.mgf_le] with ω' h h_mgf
  simpa [integrable_const_iff] using h

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
h : HasSubgaussianMGF X 0 κ ν
⊢ X =ᵐ[⇑κ ∘ₘ ν] 0

Tactic:
have hX := h.aestronglyMeasurable

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
h : HasSubgaussianMGF X 0 κ ν
hX : AEStronglyMeasurable X (⇑κ ∘ₘ ν)
⊢ X =ᵐ[⇑κ ∘ₘ ν] 0
Module: Mathlib.Probability.Moments.SubGaussian

Goal state before:
pp: case pos.integrable_exp_mul
Ω : Type u_1
Ω' : Type u_2
mΩ : MeasurableSpace Ω
mΩ' : MeasurableSpace Ω'
ν : Measure Ω'
κ : Kernel Ω' Ω
Ω'' : Type u_3
mΩ'' : MeasurableSpace Ω''
Y : Ω'' → ℝ
cY : ℝ≥0
η : Kernel Ω Ω''
h : HasSubgaussianMGF Y cY η (⇑κ ∘ₘ ν)
hν : SFinite ν
hκ : IsSFiniteKernel κ
⊢ ∀ (t : ℝ), Integrable (fun ω ↦ rexp (t * Y ω)) (⇑(prodMkLeft Ω' η) ∘ₘ ν ⊗ₘ κ)

Tactic:
simpa using h.integrable_exp_mul

Goal state after:
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
⊢ ∀ (K : ℕ), ∑ j ∈ range K, ℙ {ω | X ω ∈ Set.Ioi ↑j} ≤ ENNReal.ofReal ((∫ (a : Ω), X a) + 1)

Tactic:
intro K

Goal state after:
Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K : ℕ
⊢ ∑ j ∈ range K, ℙ {ω | X ω ∈ Set.Ioi ↑j} ≤ ENNReal.ofReal ((∫ (a : Ω), X a) + 1)
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: Ω : Type u_1
inst✝¹ : MeasureSpace Ω
inst✝ : IsProbabilityMeasure ℙ
X : Ω → ℝ
hint : Integrable X ℙ
hnonneg : 0 ≤ X
K : ℕ
Y : ℕ → Ω → ℝ := fun n ↦ truncation X ↑n
ρ : Measure ℝ := Measure.map X ℙ
Y2 : ∀ (n : ℕ), ∫ (a : Ω), (Y n ^ 2) a = ∫ (x : ℝ) in 0..↑n, x ^ 2 ∂ρ
⊢ ∑ x ∈ (range K).sigma range, (↑x.fst ^ 2)⁻¹ * ∫ (x : ℝ) in ↑x.snd..↑(x.snd + 1), x ^ 2 ∂ρ =
    ∑ x ∈ (range K).sigma fun a ↦ Ioo a K, (↑x.snd ^ 2)⁻¹ * ∫ (x : ℝ) in ↑x.fst..↑(x.fst + 1), x ^ 2 ∂ρ

Tactic:
refine sum_nbij' (fun p ↦ ⟨p.2, p.1⟩) (fun p ↦ ⟨p.2, p.1⟩) ?_ ?_ ?_ ?_ ?_ <;>
        aesop (add unsafe lt_trans)

Goal state after:
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
⊢ ∀ (K : ℕ), ∑ j ∈ range K, (↑j ^ 2)⁻¹ * Var[Y j; ℙ] ≤ 2 * ∫ (a : Ω), X 0 a

Tactic:
intro K

Goal state after:
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
K : ℕ
⊢ ∑ j ∈ range K, (↑j ^ 2)⁻¹ * Var[Y j; ℙ] ≤ 2 * ∫ (a : Ω), X 0 a
Module: Mathlib.Probability.StrongLaw

Goal state before:
pp: case e'_3.a
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)
N j : ℕ
a✝¹ : j ∈ range (u (N - 1))
hj : 0 < j
x✝ : ℕ
a✝ : x✝ ∈ {i ∈ range N | ↑j < ↑⌊c ^ i⌋₊}
⊢ (↑(u x✝) ^ 2)⁻¹ = 1 / ↑⌊c ^ x✝⌋₊ ^ 2

Tactic:
simp only [u, one_div]

Goal state after:
Module: Mathlib.RepresentationTheory.Irreducible

Goal state before:
pp: G : Type u_1
k : Type u_2
V : Type u_3
inst✝⁶ : Monoid G
inst✝⁵ : Field k
inst✝⁴ : AddCommGroup V
inst✝³ : Module k V
ρ : Representation k G V
inst✝² : ρ.IsIrreducible
inst✝¹ : FiniteDimensional k V
inst✝ : IsAlgClosed k
⊢ Bijective ⇑(algebraMap k (ρ.IntertwiningMap ρ))

Tactic:
by
  have : Bijective (algebraMap k (Module.End k[G] ρ.asModule)) :=
    IsSimpleModule.algebraMap_end_bijective_of_isAlgClosed k
  exact (Bijective.of_comp_iff' (IntertwiningMap.equivAlgEnd (ρ := ρ)).bijective _).1 this

Goal state after:
Module: Mathlib.RepresentationTheory.Character

Goal state before:
pp: G : Type u_1
k : Type u_2
V : Type u_3
inst✝⁶ : Group G
inst✝⁵ : Field k
inst✝⁴ : AddCommGroup V
inst✝³ : Module k V
inst✝² : FiniteDimensional k V
ρ : Representation k G V
inst✝¹ : Fintype G
inst✝ : Invertible ↑(Nat.card G)
⊢ (↑(Nat.card G))⁻¹ * ∑ g, ρ.character g = ↑(finrank k ↥ρ.invariants)

Tactic:
have : Invertible (Fintype.card G : k) := by rw [Fintype.card_eq_nat_card]; assumption
  rw [← (isProj_averageMap ρ).trace]
  simp [character, GroupAlgebra.average, _root_.map_sum]

Goal state after:
Module: Mathlib.RepresentationTheory.Character

Goal state before:
pp: k : Type u
inst✝⁶ : Field k
G : Type v
inst✝⁵ : Group G
inst✝⁴ : IsAlgClosed k
inst✝³ : Fintype G
inst✝² : Invertible ↑(Nat.card G)
V W : FDRep k G
inst✝¹ : Simple V
inst✝ : Simple W
⊢ ↑(if Nonempty (W ≅ V) then 1 else 0) = ↑(if Nonempty (V ≅ W) then 1 else 0)

Tactic:
Iso.nonempty_iso_symm

Goal state after:
k : Type u
inst✝⁶ : Field k
G : Type v
inst✝⁵ : Group G
inst✝⁴ : IsAlgClosed k
inst✝³ : Fintype G
inst✝² : Invertible ↑(Nat.card G)
V W : FDRep k G
inst✝¹ : Simple V
inst✝ : Simple W
⊢ ↑(if Nonempty (V ≅ W) then 1 else 0) = ↑(if Nonempty (V ≅ W) then 1 else 0)
Module: Mathlib.RepresentationTheory.Rep.Basic

Goal state before:
pp: case insert
k : Type u
G : Type v
inst✝¹ : Semiring k
inst✝ : Monoid G
A B : Rep k G
ι : Type u'
f : ι → (A ⟶ B)
a : ι
s : Finset ι
ha : a ∉ s
h : Hom.hom (∑ i ∈ s, f i) = ∑ i ∈ s, Hom.hom (f i)
⊢ Hom.hom (∑ i ∈ insert a s, f i) = ∑ i ∈ insert a s, Hom.hom (f i)

Tactic:
simp [Finset.sum_insert ha, add_hom, h]

Goal state after:
Module: Mathlib.RepresentationTheory.Homological.FiniteCyclic

Goal state before:
pp: case refine_1
k : Type u_1
G : Type u_2
inst✝² : CommRing k
inst✝¹ : Group G
inst✝ : Finite G
this : Fintype G
⊢ (Set.range fun gv ↦ ((leftRegular k G) gv.1) gv.2 - gv.2) ⊆
    ↑((linearCombination k fun x ↦ 1) ∘ₗ ↑(MonoidAlgebra.coeffLinearEquiv k)).ker

pp: case refine_2
k : Type u_1
G : Type u_2
inst✝² : CommRing k
inst✝¹ : Group G
inst✝ : Finite G
this : Fintype G
x : MonoidAlgebra k G
hx : x ∈ ((linearCombination k fun x ↦ 1) ∘ₗ ↑(MonoidAlgebra.coeffLinearEquiv k)).ker
⊢ x ∈ Coinvariants.ker (leftRegular k G)

Tactic:
· rintro x ⟨⟨g, y⟩, rfl⟩
    simpa [linearCombination, sub_eq_zero, sum_fintype]
      using Finset.sum_bijective _ (Group.mulLeft_bijective g⁻¹) (by simp) (by lia)

Goal state after:
case refine_2
k : Type u_1
G : Type u_2
inst✝² : CommRing k
inst✝¹ : Group G
inst✝ : Finite G
this : Fintype G
x : MonoidAlgebra k G
hx : x ∈ ((linearCombination k fun x ↦ 1) ∘ₗ ↑(MonoidAlgebra.coeffLinearEquiv k)).ker
⊢ x ∈ Coinvariants.ker (leftRegular k G)
Module: Mathlib.RepresentationTheory.Homological.GroupHomology.LongExactSequence

Goal state before:
pp: k G : Type u
inst✝¹ : CommRing k
inst✝ : Group G
X : ShortComplex (Rep k G)
hX : X.ShortExact
z : ↥(cycles₂ X.X₃)
y : G × G →₀ ↑X.X₂
hy : (mapRange.linearMap (Rep.Hom.hom X.g).toLinearMap) y = ↑z
x : G →₀ ↑X.X₁
hx : (mapRange.linearMap (Rep.Hom.hom X.f).toLinearMap) x = (ConcreteCategory.hom (d₂₁ X.X₂)) y
⊢ (ConcreteCategory.hom (δ hX 2 1 ⋯)) ((ConcreteCategory.hom (H2π X.X₃)) z) = (ConcreteCategory.hom (H1π X.X₁)) ⟨x, ⋯⟩

Tactic:
simpa only [H2π, ModuleCat.hom_comp, LinearMap.coe_comp, Function.comp_apply, H1π,
    ← cyclesMk₂_eq X.X₃, ← cyclesMk₁_eq X.X₁]
  using! δ_apply hX (i := 2) (j := 1) rfl ((chainsIso₂ X.X₃).inv z.1) (by
    rw [← LinearMap.comp_apply, ← ModuleCat.hom_comp, eq_d₂₁_comp_inv]; simp)
    ((chainsIso₂ X.X₂).inv y) (Finsupp.ext fun _ => by simp [chainsIso₂, ← hy])
    ((chainsIso₁ X.X₁).inv x) (Finsupp.ext fun _ => by
    conv_rhs => rw [← LinearMap.comp_apply, ← ModuleCat.hom_comp, eq_d₂₁_comp_inv]
    simp [← hx, chainsIso₁])

Goal state after:
Module: Mathlib.RepresentationTheory.Tannaka

Goal state before:
pp: k G : Type u
inst✝³ : CommRing k
inst✝² : Group G
inst✝¹ : Finite G
inst✝ : Nontrivial k
s t : G
h : (equivHom k G) s = (equivHom k G) t
⊢ s = t

Tactic:
apply_fun (fun x ↦ (x.hom.hom.app rightFDRep).hom (single t 1) 1) at h
  simp_all [single_apply]

Goal state after:
Module: Mathlib.RingTheory.PowerSeries.Ideal

Goal state before:
pp: case pos
R : Type u_1
inst✝ : CommRing R
I : Ideal R⟦X⟧
hI : X ∈ I
hfg : (Ideal.map constantCoeff I).FG
⊢ spanFinrank I ≤ spanFinrank (Ideal.map constantCoeff I) + 1

Tactic:
nth_rw 1 [eq_span_insert_X_of_X_mem_of_span_eq hI (I.map constantCoeff).span_generators]

Goal state after:
case pos
R : Type u_1
inst✝ : CommRing R
I : Ideal R⟦X⟧
hI : X ∈ I
hfg : (Ideal.map constantCoeff I).FG
⊢ spanFinrank (Ideal.span (insert X (⇑C '' generators (Ideal.map constantCoeff I)))) ≤
    spanFinrank (Ideal.map constantCoeff I) + 1
Module: Mathlib.Topology.Algebra.Nonarchimedean.AdicTopology

Goal state before:
pp: R : Type u_1
inst✝ : CommRing R
I : Ideal R
x : R
this : TopologicalSpace R := I.adicTopology
⊢ (𝓝 x).HasBasis (fun _n ↦ True) fun n ↦ (fun y ↦ x + y) '' ↑(I ^ n)

Tactic:
have := I.hasBasis_nhds_zero_adic.map fun y => x + y

Goal state after:
R : Type u_1
inst✝ : CommRing R
I : Ideal R
x : R
this✝ : TopologicalSpace R := I.adicTopology
this : (Filter.map (fun y ↦ x + y) (𝓝 0)).HasBasis (fun _n ↦ True) fun i ↦ (fun y ↦ x + y) '' ↑(I ^ i)
⊢ (𝓝 x).HasBasis (fun _n ↦ True) fun n ↦ (fun y ↦ x + y) '' ↑(I ^ n)
Module: Mathlib.Topology.Algebra.Nonarchimedean.AdicTopology

Goal state before:
pp: case mpr.h
R : Type u_1
inst✝¹ : CommRing R
top : TopologicalSpace R
inst✝ : IsTopologicalRing R
J : Ideal R
H₁ : ∀ (n : ℕ), IsOpen[top] ↑(J ^ n)
H₂ : ∀ s ∈ 𝓝 0, ∃ n, ↑(J ^ n) ⊆ s
s : Set R
this : SubmodulesRingBasis fun n ↦ J ^ n • ⊤ := Ideal.adic_basis J
⊢ s ∈ 𝓝 0 ↔ s ∈ 𝓝 0

Tactic:
J.hasBasis_nhds_zero_adic.mem_iff

Goal state after:
case mpr.h
R : Type u_1
inst✝¹ : CommRing R
top : TopologicalSpace R
inst✝ : IsTopologicalRing R
J : Ideal R
H₁ : ∀ (n : ℕ), IsOpen[top] ↑(J ^ n)
H₂ : ∀ s ∈ 𝓝 0, ∃ n, ↑(J ^ n) ⊆ s
s : Set R
this : SubmodulesRingBasis fun n ↦ J ^ n • ⊤ := Ideal.adic_basis J
⊢ s ∈ 𝓝 0 ↔ ∃ i, True ∧ ↑(J ^ i) ⊆ s