mathlib-initiative/mathlib-tactics huggingface dataset:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f : V →ₗ[A] W
g₁ g₂ : ρ.IntertwiningMap σ
⊢ { toFun := ⇑(g₁ + g₂).toLinearMap, map_add' := ⋯, map_smul' := ⋯ } =
    { toFun := ⇑g₁.toLinearMap, map_add' := ⋯, map_smul' := ⋯ } +
      { toFun := ⇑g₂.toLinearMap, map_add' := ⋯, map_smul' := ⋯ }

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f : V →ₗ[A] W
t : A
g : ρ.IntertwiningMap σ
⊢ { toFun := ⇑(t • g).toLinearMap, map_add' := ⋯, map_smul' := ⋯ } =
    (RingHom.id A) t • { toFun := ⇑g.toLinearMap, map_add' := ⋯, map_smul' := ⋯ }

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f✝ : V →ₗ[A] W
f : ρ.asModule →ₗ[A[G]] σ.asModule
a : A
v : V
⊢ f.toFun (a • v) = (RingHom.id A) a • f.toFun v

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝ : IsProbabilityMeasure μ
a b : ℝ
hm : AEMeasurable X μ
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
hc : ∫ (x : Ω), X x ∂μ = 0
t : ℝ
ht : t < 0
⊢ mgf X μ t = mgf (-X) μ (-t)

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝ : IsProbabilityMeasure μ
a b : ℝ
hm : AEMeasurable X μ
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
hc : ∫ (x : Ω), X x ∂μ = 0
t : ℝ
ht : t < 0
ω : Ω
⊢ X ω ∈ Set.Icc a b → (-X) ω ∈ Set.Icc (-b) (-a)

Tactic:
⟨hl, hr⟩

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝ : IsProbabilityMeasure μ
a b : ℝ
hm : AEMeasurable X μ
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
hc : ∫ (x : Ω), X x ∂μ = 0
t : ℝ
ht : t < 0
ω : Ω
hl : a ≤ X ω
hr : X ω ≤ b
⊢ (-X) ω ∈ Set.Icc (-b) (-a)
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f✝ : V →ₗ[A] W
f : ρ.asModule →ₗ[A[G]] σ.asModule
g : G
⊢ { toAddHom := f.toAddHom, map_smul' := ⋯ } ∘ₗ ρ g = σ g ∘ₗ { toAddHom := f.toAddHom, map_smul' := ⋯ }

Tactic:
by

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
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 : ℕ
⊢ ∑ j ∈ range (u (N - 1)), (∑ i ∈ range N with j < u i, (↑(u i) ^ 2)⁻¹) * Var[Y j; ℙ] ≤
    ∑ j ∈ range (u (N - 1)), c ^ 5 * (c - 1)⁻¹ ^ 3 / ↑j ^ 2 * Var[Y j; ℙ]

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝ : IsProbabilityMeasure μ
a b : ℝ
hm : AEMeasurable X μ
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
hc : ∫ (x : Ω), X x ∂μ = 0
t : ℝ
ht : t < 0
⊢ mgf (-X) μ (-t) ≤ rexp ((↑‖-a - -b‖₊ / 2) ^ 2 * (-t) ^ 2 / 2)

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝ : IsProbabilityMeasure μ
a b : ℝ
hm : AEMeasurable X μ
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
hc : ∫ (x : Ω), X x ∂μ = 0
t : ℝ
ht : t < 0
⊢ rexp ((↑‖-a - -b‖₊ / 2) ^ 2 * (-t) ^ 2 / 2) = rexp ((↑‖b - a‖₊ / 2) ^ 2 * t ^ 2 / 2)

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝ : IsProbabilityMeasure μ
a b : ℝ
hm : AEMeasurable X μ
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
hc : ∫ (x : Ω), X x ∂μ = 0
t : ℝ
⊢ mgf X μ t ≤ rexp (↑((‖b - a‖₊ / 2) ^ 2) * t ^ 2 / 2)

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f✝ : V →ₗ[A] W
f : σ.IntertwiningMap τ
g : ρ.IntertwiningMap σ
⊢ ∀ (g_1 : G) (v : V), (f.toLinearMap ∘ₗ g.toLinearMap) ((ρ g_1) v) = (τ g_1) ((f.toLinearMap ∘ₗ g.toLinearMap) v)

Tactic:
by

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
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 : ℕ
⊢ ∑ j ∈ range (u (N - 1)), c ^ 5 * (c - 1)⁻¹ ^ 3 / ↑j ^ 2 * Var[Y j; ℙ] =
    c ^ 5 * (c - 1)⁻¹ ^ 3 * ∑ j ∈ range (u (N - 1)), (↑j ^ 2)⁻¹ * Var[Y j; ℙ]

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝ : IsProbabilityMeasure μ
a b : ℝ
hm : AEMeasurable X μ
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
ω : Ω
hab : X ω ∈ Set.Icc a b
⊢ X ω - ∫ (x : Ω), X x ∂μ ∈ Set.Icc (a - ∫ (x : Ω), X x ∂μ) (b - ∫ (x : Ω), X x ∂μ)

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f✝ : V →ₗ[A] W
f : σ.IntertwiningMap τ
x✝¹ x✝ : ρ.IntertwiningMap σ
⊢ LinearMap.intertwiningMap_of_isIntertwiningMap ρ τ (f.toLinearMap ∘ₗ (x✝¹ + x✝).toLinearMap) ⋯ =
    LinearMap.intertwiningMap_of_isIntertwiningMap ρ τ (f.toLinearMap ∘ₗ x✝¹.toLinearMap) ⋯ +
      LinearMap.intertwiningMap_of_isIntertwiningMap ρ τ (f.toLinearMap ∘ₗ x✝.toLinearMap) ⋯

Tactic:
by

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
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 : ℕ
⊢ c ^ 5 * (c - 1)⁻¹ ^ 3 * ∑ j ∈ range (u (N - 1)), (↑j ^ 2)⁻¹ * Var[Y j; ℙ] ≤
    c ^ 5 * (c - 1)⁻¹ ^ 3 * (2 * ∫ (a : Ω), X 0 a)

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝ : IsProbabilityMeasure μ
a b : ℝ
hm : AEMeasurable X μ
hb : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b
⊢ HasSubgaussianMGF (fun ω ↦ X ω - ∫ (x : Ω), X x ∂μ) ((‖b - a‖₊ / 2) ^ 2) μ

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f✝ : V →ₗ[A] W
f : σ.IntertwiningMap τ
x✝¹ : A
x✝ : ρ.IntertwiningMap σ
⊢ LinearMap.intertwiningMap_of_isIntertwiningMap ρ τ (f.toLinearMap ∘ₗ (x✝¹ • x✝).toLinearMap) ⋯ =
    (RingHom.id A) x✝¹ • LinearMap.intertwiningMap_of_isIntertwiningMap ρ τ (f.toLinearMap ∘ₗ x✝.toLinearMap) ⋯

Tactic:
by

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
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 : ℕ), ∑ i ∈ range N, (↑(u i) ^ 2)⁻¹ * Var[S (u i); ℙ] ≤ C

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f : V →ₗ[A] W
x✝¹ x✝ : σ.IntertwiningMap τ
⊢ { toFun := fun g ↦ LinearMap.intertwiningMap_of_isIntertwiningMap ρ τ ((x✝¹ + x✝).toLinearMap ∘ₗ g.toLinearMap) ⋯,
      map_add' := ⋯, map_smul' := ⋯ } =
    { toFun := fun g ↦ LinearMap.intertwiningMap_of_isIntertwiningMap ρ τ (x✝¹.toLinearMap ∘ₗ g.toLinearMap) ⋯,
        map_add' := ⋯, map_smul' := ⋯ } +
      { toFun := fun g ↦ LinearMap.intertwiningMap_of_isIntertwiningMap ρ τ (x✝.toLinearMap ∘ₗ g.toLinearMap) ⋯,
        map_add' := ⋯, map_smul' := ⋯ }

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
m mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝¹ : StandardBorelSpace Ω
inst✝ : IsFiniteMeasure μ
Y : Ω → ℝ
cX cY : ℝ≥0
hm : m ≤ mΩ
hX : HasSubgaussianMGF X cX (μ.trim hm)
hY : HasCondSubgaussianMGF m hm Y cY μ
this : HasSubgaussianMGF (fun p ↦ X p.1 + Y p.2) (cX + cY) (Measure.map Function.diag μ)
⊢ HasSubgaussianMGF (X + Y) (cX + cY) μ

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f : V →ₗ[A] W
x✝¹ : A
x✝ : σ.IntertwiningMap τ
⊢ { toFun := fun g ↦ LinearMap.intertwiningMap_of_isIntertwiningMap ρ τ ((x✝¹ • x✝).toLinearMap ∘ₗ g.toLinearMap) ⋯,
      map_add' := ⋯, map_smul' := ⋯ } =
    (RingHom.id A) x✝¹ •
      { toFun := fun g ↦ LinearMap.intertwiningMap_of_isIntertwiningMap ρ τ (x✝.toLinearMap ∘ₗ g.toLinearMap) ⋯,
        map_add' := ⋯, map_smul' := ⋯ }

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
m mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝¹ : StandardBorelSpace Ω
inst✝ : IsFiniteMeasure μ
Y : Ω → ℝ
cX cY : ℝ≥0
hm : m ≤ mΩ
hX : Kernel.HasSubgaussianMGF X cX (Kernel.const Unit (μ.trim hm)) (Measure.dirac ())
hY : HasCondSubgaussianMGF m hm Y cY μ
⊢ Kernel.HasSubgaussianMGF Y cY (condExpKernel μ m) (⇑(Kernel.const Unit (μ.trim hm)) ∘ₘ Measure.dirac ())

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
a : A
f : σ.IntertwiningMap τ
g : ρ.IntertwiningMap σ
⊢ (a • f).comp g = a • f.comp g

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
m mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝¹ : StandardBorelSpace Ω
inst✝ : IsFiniteMeasure μ
Y : Ω → ℝ
cX cY : ℝ≥0
hm : m ≤ mΩ
hX : Kernel.HasSubgaussianMGF X cX (Kernel.const Unit (μ.trim hm)) (Measure.dirac ())
hY : HasCondSubgaussianMGF m hm Y cY μ
hY' : Kernel.HasSubgaussianMGF Y cY (condExpKernel μ m) (⇑(Kernel.const Unit (μ.trim hm)) ∘ₘ Measure.dirac ())
⊢ Kernel.HasSubgaussianMGF (fun p ↦ X p.1 + Y p.2) (cX + cY) (Kernel.const Unit (Measure.map Function.diag μ))
    (Measure.dirac ())

Tactic:
convert! hX.add_comp hY'

Goal state after:
case e'_7
Ω : Type u_1
m mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝¹ : StandardBorelSpace Ω
inst✝ : IsFiniteMeasure μ
Y : Ω → ℝ
cX cY : ℝ≥0
hm : m ≤ mΩ
hX : Kernel.HasSubgaussianMGF X cX (Kernel.const Unit (μ.trim hm)) (Measure.dirac ())
hY : HasCondSubgaussianMGF m hm Y cY μ
hY' : Kernel.HasSubgaussianMGF Y cY (condExpKernel μ m) (⇑(Kernel.const Unit (μ.trim hm)) ∘ₘ Measure.dirac ())
e_3✝ : m.prod mΩ = Prod.instMeasurableSpace
⊢ Kernel.const Unit (Measure.map Function.diag μ) =
    Kernel.const Unit (μ.trim hm) ⊗ₖ Kernel.prodMkLeft Unit (condExpKernel μ m)
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
a : A
f : σ.IntertwiningMap τ
g : ρ.IntertwiningMap σ
⊢ f.comp (a • g) = a • f.comp g

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f✝ : V →ₗ[A] W
f : ρ.IntertwiningMap ρ
n : ℕ
⊢ (f ^ n).toLinearMap = f.toLinearMap ^ n

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
m mΩ : MeasurableSpace Ω
μ : Measure Ω
X : Ω → ℝ
inst✝¹ : StandardBorelSpace Ω
inst✝ : IsFiniteMeasure μ
Y : Ω → ℝ
cX cY : ℝ≥0
hm : m ≤ mΩ
hX : HasSubgaussianMGF X cX (μ.trim hm)
hY : HasCondSubgaussianMGF m hm Y cY μ
⊢ HasSubgaussianMGF (X + Y) (cX + cY) μ

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f : V →ₗ[A] W
⊢ ∀ (x : ρ.IntertwiningMap ρ) (n : ℕ), (x ^ n).toLinearMap = x.toLinearMap ^ n

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f✝ : V →ₗ[A] W
a : A
f g : ρ.IntertwiningMap ρ
⊢ f * a • g = a • (f * g)

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝¹ : StandardBorelSpace Ω
Y : ℕ → Ω → ℝ
cY : ℕ → ℝ≥0
ℱ : Filtration ℕ mΩ
inst✝ : IsZeroOrProbabilityMeasure μ
h_adapted : StronglyAdapted ℱ Y
h0 : HasSubgaussianMGF (Y 0) (cY 0) μ
n : ℕ
a✝ :
  ((∀ i < n - 1, HasCondSubgaussianMGF (↑ℱ i) ⋯ (Y (i + 1)) (cY (i + 1)) μ) →
      HasSubgaussianMGF (fun ω ↦ ∑ i ∈ Finset.range n, Y i ω) (∑ i ∈ Finset.range n, cY i) μ) →
    (∀ i < n + 1 - 1, HasCondSubgaussianMGF (↑ℱ i) ⋯ (Y (i + 1)) (cY (i + 1)) μ) →
      HasSubgaussianMGF (fun ω ↦ ∑ i ∈ Finset.range (n + 1), Y i ω) (∑ i ∈ Finset.range (n + 1), cY i) μ
hn :
  (∀ i < n + 1 - 1, HasCondSubgaussianMGF (↑ℱ i) ⋯ (Y (i + 1)) (cY (i + 1)) μ) →
    HasSubgaussianMGF (fun ω ↦ ∑ i ∈ Finset.range (n + 1), Y i ω) (∑ i ∈ Finset.range (n + 1), cY i) μ
h_subG : ∀ i < n + 1 + 1 - 1, HasCondSubgaussianMGF (↑ℱ i) ⋯ (Y (i + 1)) (cY (i + 1)) μ
i : ℕ
hi : i < n + 1 - 1
⊢ i < n + 1 + 1 - 1

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f : V →ₗ[A] W
⊢ ∀ (x y : ρ.IntertwiningMap ρ),
    (equivLinearMapAsModule ρ ρ) (x * y) = (equivLinearMapAsModule ρ ρ) x * (equivLinearMapAsModule ρ ρ) y

Tactic:
by

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
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 : ℕ
⊢ ∑ i ∈ range N, ℙ {ω | ↑(u i) * ε ≤ |S (u i) ω - ∫ (a : Ω), S (u i) a|} ≤
    ∑ i ∈ range N, ENNReal.ofReal (Var[S (u i); ℙ] / (↑(u i) * ε) ^ 2)

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
inst✝³ : CommSemiring A
inst✝² : Monoid G
inst✝¹ : AddCommMonoid V
inst✝ : Module A V
ρ : Representation A G V
g : G
hg : g ∈ Submonoid.center G
⊢ ρ.IsIntertwiningMap ρ (ρ g)

Tactic:
by

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

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
μ : Measure Ω
inst✝¹ : StandardBorelSpace Ω
Y : ℕ → Ω → ℝ
cY : ℕ → ℝ≥0
ℱ : Filtration ℕ mΩ
inst✝ : IsZeroOrProbabilityMeasure μ
h_adapted : StronglyAdapted ℱ Y
h0 : HasSubgaussianMGF (Y 0) (cY 0) μ
n : ℕ
a✝ :
  ((∀ i < n - 1, HasCondSubgaussianMGF (↑ℱ i) ⋯ (Y (i + 1)) (cY (i + 1)) μ) →
      HasSubgaussianMGF (fun ω ↦ ∑ i ∈ Finset.range n, Y i ω) (∑ i ∈ Finset.range n, cY i) μ) →
    (∀ i < n + 1 - 1, HasCondSubgaussianMGF (↑ℱ i) ⋯ (Y (i + 1)) (cY (i + 1)) μ) →
      HasSubgaussianMGF (fun ω ↦ ∑ i ∈ Finset.range (n + 1), Y i ω) (∑ i ∈ Finset.range (n + 1), cY i) μ
hn : HasSubgaussianMGF (fun ω ↦ ∑ i ∈ Finset.range (n + 1), Y i ω) (∑ i ∈ Finset.range (n + 1), cY i) μ
h_subG : ∀ i < n + 1 + 1 - 1, HasCondSubgaussianMGF (↑ℱ i) ⋯ (Y (i + 1)) (cY (i + 1)) μ
⊢ n < n + 1 + 1 - 1

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f : V →ₗ[A] W
z : A[G]
hz : z ∈ Submonoid.center A[G]
x✝ : G
⊢ ρ.asAlgebraHom z ∘ₗ ρ x✝ = ρ x✝ ∘ₗ ρ.asAlgebraHom z

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f : V →ₗ[A] W
⊢ centralAlgebraMul ρ ⋯ = 1

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁷ : CommSemiring A
inst✝⁶ : Monoid G
inst✝⁵ : AddCommMonoid V
inst✝⁴ : AddCommMonoid W
inst✝³ : AddCommMonoid U
inst✝² : Module A V
inst✝¹ : Module A W
inst✝ : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f : V →ₗ[A] W
x✝¹ x✝ : ↥(Submonoid.center A[G])
⊢ centralAlgebraMul ρ ⋯ = centralAlgebraMul ρ ⋯ * centralAlgebraMul ρ ⋯

Tactic:
by

Goal state after:
Module: Mathlib.RepresentationTheory.Intertwining

Goal state before:
pp: A : Type u_1
G : Type u_2
V : Type u_3
W : Type u_4
U : Type u_5
inst✝⁹ : CommSemiring A
inst✝⁸ : Monoid G
inst✝⁷ : AddCommMonoid V
inst✝⁶ : AddCommMonoid W
inst✝⁵ : AddCommMonoid U
inst✝⁴ : Module A V
inst✝³ : Module A W
inst✝² : Module A U
ρ : Representation A G V
σ : Representation A G W
τ : Representation A G U
f✝ : V →ₗ[A] W
P : Type u_6
inst✝¹ : AddCommMonoid P
inst✝ : Module A P
π : Representation A G P
f : ρ.IntertwiningMap σ
g : τ.IntertwiningMap π
x : G
⊢ TensorProduct.map f.toLinearMap g.toLinearMap ∘ₗ (ρ.tprod τ) x =
    (σ.tprod π) x ∘ₗ TensorProduct.map f.toLinearMap g.toLinearMap

Tactic:
by

Goal state after: