mathlib-initiative/mathlib-tactics huggingface dataset:
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_int : Integrable (fun ω ↦ 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ
⊢ ∫ (ω : Ω), X ω ^ 2 * rexp (v * X ω) ∂μ - (2 * ∫ (ω : Ω), X ω * rexp (v * X ω) ∂μ) * deriv (cgf X μ) v +
      deriv (cgf X μ) v ^ 2 * mgf X μ v =
    ∫ (a : Ω), X a ^ 2 * rexp (v * X a) - 2 * X a * deriv (cgf X μ) v * rexp (v * X a) ∂μ +
      ∫ (a : Ω), deriv (cgf X μ) v ^ 2 * rexp (v * X a) ∂μ

pp: case e_a.hf
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_int : Integrable (fun ω ↦ 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ
⊢ Integrable (fun ω ↦ X ω ^ 2 * rexp (v * X ω) - 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ

pp: case e_a.hg
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_int : Integrable (fun ω ↦ 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ
⊢ Integrable (fun ω ↦ deriv (cgf X μ) v ^ 2 * rexp (v * X ω)) μ

Tactic:
]

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_int : Integrable (fun ω ↦ 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ
⊢ ∫ (ω : Ω), X ω ^ 2 * rexp (v * X ω) ∂μ - (2 * ∫ (ω : Ω), X ω * rexp (v * X ω) ∂μ) * deriv (cgf X μ) v +
      deriv (cgf X μ) v ^ 2 * mgf X μ v =
    ∫ (a : Ω), X a ^ 2 * rexp (v * X a) - 2 * X a * deriv (cgf X μ) v * rexp (v * X a) ∂μ +
      ∫ (a : Ω), deriv (cgf X μ) v ^ 2 * rexp (v * X a) ∂μ

case e_a.hf
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_int : Integrable (fun ω ↦ 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ
⊢ Integrable (fun ω ↦ X ω ^ 2 * rexp (v * X ω) - 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ

case e_a.hg
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_int : Integrable (fun ω ↦ 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ
⊢ Integrable (fun ω ↦ deriv (cgf X μ) v ^ 2 * rexp (v * X ω)) μ
Module: Mathlib.Probability.Moments.IntegrableExpMul

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
z : ℂ
hz : z.re ∈ interior (integrableExpSet X μ)
p : ℝ
hp : 0 ≤ p
hX : AEMeasurable X μ
⊢ Integrable (fun a ↦ ‖↑(X a ^ p) * cexp (z * ↑(X a))‖) μ

pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
z : ℂ
hz : z.re ∈ interior (integrableExpSet X μ)
p : ℝ
hp : 0 ≤ p
hX : AEMeasurable X μ
⊢ AEStronglyMeasurable (fun ω ↦ ↑(X ω ^ p) * cexp (z * ↑(X ω))) μ

Tactic:
]

Goal state after:
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
z : ℂ
hz : z.re ∈ interior (integrableExpSet X μ)
p : ℝ
hp : 0 ≤ p
hX : AEMeasurable X μ
⊢ Integrable (fun a ↦ ‖↑(X a ^ p) * cexp (z * ↑(X a))‖) μ

Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
z : ℂ
hz : z.re ∈ interior (integrableExpSet X μ)
p : ℝ
hp : 0 ≤ p
hX : AEMeasurable X μ
⊢ AEStronglyMeasurable (fun ω ↦ ↑(X ω ^ p) * cexp (z * ↑(X ω))) μ
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_int : Integrable (fun ω ↦ 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ
⊢ ∫ (ω : Ω), X ω ^ 2 * rexp (v * X ω) ∂μ - (2 * ∫ (ω : Ω), X ω * rexp (v * X ω) ∂μ) * deriv (cgf X μ) v +
      deriv (cgf X μ) v ^ 2 * mgf X μ v =
    ∫ (a : Ω), X a ^ 2 * rexp (v * X a) ∂μ - ∫ (a : Ω), 2 * X a * deriv (cgf X μ) v * rexp (v * X a) ∂μ +
      ∫ (a : Ω), deriv (cgf X μ) v ^ 2 * rexp (v * X a) ∂μ

Tactic:
]

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_int : Integrable (fun ω ↦ 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ
⊢ ∫ (ω : Ω), X ω ^ 2 * rexp (v * X ω) ∂μ - (2 * ∫ (ω : Ω), X ω * rexp (v * X ω) ∂μ) * deriv (cgf X μ) v +
      deriv (cgf X μ) v ^ 2 * mgf X μ v =
    ∫ (a : Ω), X a ^ 2 * rexp (v * X a) ∂μ - ∫ (a : Ω), 2 * X a * deriv (cgf X μ) v * rexp (v * X a) ∂μ +
      ∫ (a : Ω), deriv (cgf X μ) v ^ 2 * rexp (v * X a) ∂μ
Module: Mathlib.Probability.Distributions.Gaussian.Basic

Goal state before:
pp: m : ℝ
v : ℝ≥0
L : StrongDual ℝ ℝ
⊢ gaussianReal (L m) ((L 1 ^ 2).toNNReal * v) =
    gaussianReal (∫ (x : ℝ), L x ∂gaussianReal m v) Var[⇑L; gaussianReal m v].toNNReal

Tactic:
]

Goal state after:
m : ℝ
v : ℝ≥0
L : StrongDual ℝ ℝ
⊢ gaussianReal (L m) ((L 1 ^ 2).toNNReal * v) =
    gaussianReal (∫ (x : ℝ), L x ∂gaussianReal m v) Var[⇑L; gaussianReal m v].toNNReal
Module: Mathlib.Probability.Distributions.Gaussian.Basic

Goal state before:
pp: case e_v
m : ℝ
v : ℝ≥0
L : StrongDual ℝ ℝ
⊢ (L 1 ^ 2).toNNReal * v = (max (L 1 ^ 2) 0).toNNReal * v

Tactic:
]

Goal state after:
case e_v
m : ℝ
v : ℝ≥0
L : StrongDual ℝ ℝ
⊢ (L 1 ^ 2).toNNReal * v = (max (L 1 ^ 2) 0).toNNReal * v
Module: Mathlib.Probability.Distributions.Gaussian.Basic

Goal state before:
pp: μ : Measure ℝ
h : IsGaussian μ
⊢ gaussianReal (∫ (x : ℝ), (ContinuousLinearMap.id ℝ ℝ) x ∂μ) Var[⇑(ContinuousLinearMap.id ℝ ℝ); μ].toNNReal =
    gaussianReal (∫ (x : ℝ), id x ∂μ) Var[id; μ].toNNReal

Tactic:
]

Goal state after:
μ : Measure ℝ
h : IsGaussian μ
⊢ gaussianReal (∫ (x : ℝ), (ContinuousLinearMap.id ℝ ℝ) x ∂μ) Var[⇑(ContinuousLinearMap.id ℝ ℝ); μ].toNNReal =
    gaussianReal (∫ (x : ℝ), id x ∂μ) Var[id; μ].toNNReal
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
p : Measure Ω
μ : ℝ
v : ℝ≥0
X : Ω → ℝ
hX : Measure.map X p = gaussianReal μ v
z : ℂ
⊢ NeZero (gaussianReal μ v)

Tactic:
]

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
p : Measure Ω
μ : ℝ
v : ℝ≥0
X : Ω → ℝ
hX : Measure.map X p = gaussianReal μ v
z : ℂ
⊢ NeZero (gaussianReal μ v)
Module: Mathlib.Probability.Moments.MGFAnalytic

Goal state before:
pp: case e_a.e_a.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_int : Integrable (fun ω ↦ 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ
⊢ ∫ (a : Ω), 2 * (X a * rexp (v * X a)) * deriv (cgf X μ) v ∂μ =
    ∫ (a : Ω), 2 * X a * deriv (cgf X μ) v * rexp (v * X a) ∂μ

Tactic:
]

Goal state after:
case e_a.e_a.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_int : Integrable (fun ω ↦ 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ
⊢ ∫ (a : Ω), 2 * (X a * rexp (v * X a)) * deriv (cgf X μ) v ∂μ =
    ∫ (a : Ω), 2 * X a * deriv (cgf X μ) v * rexp (v * X a) ∂μ
Module: Mathlib.Probability.Distributions.Gaussian.Real

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

Tactic:
]

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

Goal state before:
pp: μ : ℝ
v : ℝ≥0
t : ℝ
⊢ cexp (↑t * I * ↑μ + ↑↑v * (↑t * I) ^ 2 / 2) = cexp (↑t * ↑μ * I - ↑↑v * ↑t ^ 2 / 2)

Tactic:
]

Goal state after:
μ : ℝ
v : ℝ≥0
t : ℝ
⊢ cexp (↑t * I * ↑μ + ↑↑v * (↑t * I) ^ 2 / 2) = cexp (↑t * ↑μ * I - ↑↑v * ↑t ^ 2 / 2)
Module: Mathlib.Probability.Distributions.Gaussian.Basic

Goal state before:
pp: E : Type u_1
inst✝³ : TopologicalSpace E
inst✝² : AddCommMonoid E
inst✝¹ : Module ℝ E
mE : MeasurableSpace E
inst✝ : OpensMeasurableSpace E
μ : Measure E
h : ∀ (L : E →L[ℝ] ℝ), IsGaussian (Measure.map (⇑L) μ)
L : StrongDual ℝ E
⊢ gaussianReal (∫ (x : E), id (L x) ∂μ) Var[id ∘ ⇑L; μ].toNNReal = gaussianReal (∫ (x : E), L x ∂μ) Var[⇑L; μ].toNNReal

pp: case hX
E : Type u_1
inst✝³ : TopologicalSpace E
inst✝² : AddCommMonoid E
inst✝¹ : Module ℝ E
mE : MeasurableSpace E
inst✝ : OpensMeasurableSpace E
μ : Measure E
h : ∀ (L : E →L[ℝ] ℝ), IsGaussian (Measure.map (⇑L) μ)
L : StrongDual ℝ E
⊢ AEMeasurable id (Measure.map (⇑L) μ)

pp: case hY
E : Type u_1
inst✝³ : TopologicalSpace E
inst✝² : AddCommMonoid E
inst✝¹ : Module ℝ E
mE : MeasurableSpace E
inst✝ : OpensMeasurableSpace E
μ : Measure E
h : ∀ (L : E →L[ℝ] ℝ), IsGaussian (Measure.map (⇑L) μ)
L : StrongDual ℝ E
⊢ AEMeasurable (⇑L) μ

pp: case hφ
E : Type u_1
inst✝³ : TopologicalSpace E
inst✝² : AddCommMonoid E
inst✝¹ : Module ℝ E
mE : MeasurableSpace E
inst✝ : OpensMeasurableSpace E
μ : Measure E
h : ∀ (L : E →L[ℝ] ℝ), IsGaussian (Measure.map (⇑L) μ)
L : StrongDual ℝ E
⊢ AEMeasurable (⇑L) μ

pp: case hfm
E : Type u_1
inst✝³ : TopologicalSpace E
inst✝² : AddCommMonoid E
inst✝¹ : Module ℝ E
mE : MeasurableSpace E
inst✝ : OpensMeasurableSpace E
μ : Measure E
h : ∀ (L : E →L[ℝ] ℝ), IsGaussian (Measure.map (⇑L) μ)
L : StrongDual ℝ E
⊢ AEStronglyMeasurable id (Measure.map (⇑L) μ)

Tactic:
]

Goal state after:
E : Type u_1
inst✝³ : TopologicalSpace E
inst✝² : AddCommMonoid E
inst✝¹ : Module ℝ E
mE : MeasurableSpace E
inst✝ : OpensMeasurableSpace E
μ : Measure E
h : ∀ (L : E →L[ℝ] ℝ), IsGaussian (Measure.map (⇑L) μ)
L : StrongDual ℝ E
⊢ gaussianReal (∫ (x : E), id (L x) ∂μ) Var[id ∘ ⇑L; μ].toNNReal = gaussianReal (∫ (x : E), L x ∂μ) Var[⇑L; μ].toNNReal

case hX
E : Type u_1
inst✝³ : TopologicalSpace E
inst✝² : AddCommMonoid E
inst✝¹ : Module ℝ E
mE : MeasurableSpace E
inst✝ : OpensMeasurableSpace E
μ : Measure E
h : ∀ (L : E →L[ℝ] ℝ), IsGaussian (Measure.map (⇑L) μ)
L : StrongDual ℝ E
⊢ AEMeasurable id (Measure.map (⇑L) μ)

case hY
E : Type u_1
inst✝³ : TopologicalSpace E
inst✝² : AddCommMonoid E
inst✝¹ : Module ℝ E
mE : MeasurableSpace E
inst✝ : OpensMeasurableSpace E
μ : Measure E
h : ∀ (L : E →L[ℝ] ℝ), IsGaussian (Measure.map (⇑L) μ)
L : StrongDual ℝ E
⊢ AEMeasurable (⇑L) μ

case hφ
E : Type u_1
inst✝³ : TopologicalSpace E
inst✝² : AddCommMonoid E
inst✝¹ : Module ℝ E
mE : MeasurableSpace E
inst✝ : OpensMeasurableSpace E
μ : Measure E
h : ∀ (L : E →L[ℝ] ℝ), IsGaussian (Measure.map (⇑L) μ)
L : StrongDual ℝ E
⊢ AEMeasurable (⇑L) μ

case hfm
E : Type u_1
inst✝³ : TopologicalSpace E
inst✝² : AddCommMonoid E
inst✝¹ : Module ℝ E
mE : MeasurableSpace E
inst✝ : OpensMeasurableSpace E
μ : Measure E
h : ∀ (L : E →L[ℝ] ℝ), IsGaussian (Measure.map (⇑L) μ)
L : StrongDual ℝ E
⊢ AEStronglyMeasurable id (Measure.map (⇑L) μ)
Module: Mathlib.Probability.Moments.MGFAnalytic

Goal state before:
pp: case e_a.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_int : Integrable (fun ω ↦ 2 * X ω * deriv (cgf X μ) v * rexp (v * X ω)) μ
⊢ deriv (cgf X μ) v ^ 2 * ∫ (x : Ω), (fun ω ↦ rexp (v * X ω)) x ∂μ =
    deriv (cgf X μ) v ^ 2 * ∫ (a : Ω), rexp (v * X a) ∂μ

Tactic:
]

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

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
Ω' : Type u_3
mΩ' : MeasurableSpace Ω'
Y : Ω' → ℝ
μ' : Measure Ω'
inst✝¹ : IsFiniteMeasure μ
inst✝ : IsFiniteMeasure μ'
hX : AEMeasurable X μ
hY : AEMeasurable Y μ'
h : ∀ (x : ℂ), complexMGF X μ x = complexMGF Y μ' x
inner_ne_zero : ∀ (x : ℝ), x ≠ 0 → (innerₗ ℝ) x ≠ 0
w : ℝ
⊢ ∫ (v : ℝ), (BoundedContinuousFunction.char continuous_probChar ⋯ w) v ∂Measure.map X μ =
    ∫ (v : ℝ), (BoundedContinuousFunction.char continuous_probChar ⋯ w) v ∂Measure.map Y μ'

Tactic:
]

Goal state after:
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
Ω' : Type u_3
mΩ' : MeasurableSpace Ω'
Y : Ω' → ℝ
μ' : Measure Ω'
inst✝¹ : IsFiniteMeasure μ
inst✝ : IsFiniteMeasure μ'
hX : AEMeasurable X μ
hY : AEMeasurable Y μ'
h : ∀ (x : ℂ), complexMGF X μ x = complexMGF Y μ' x
inner_ne_zero : ∀ (x : ℝ), x ≠ 0 → (innerₗ ℝ) x ≠ 0
w : ℝ
⊢ ∫ (v : ℝ), (BoundedContinuousFunction.char continuous_probChar ⋯ w) v ∂Measure.map X μ =
    ∫ (v : ℝ), (BoundedContinuousFunction.char continuous_probChar ⋯ w) v ∂Measure.map Y μ'
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: Ω : Type u_1
mΩ : MeasurableSpace Ω
p : Measure Ω
μ : ℝ
v : ℝ≥0
X : Ω → ℝ
hX : Measure.map X p = gaussianReal μ v
t : ℝ
⊢ NeZero (gaussianReal μ v)

Tactic:
]

Goal state after:
Ω : Type u_1
mΩ : MeasurableSpace Ω
p : Measure Ω
μ : ℝ
v : ℝ≥0
X : Ω → ℝ
hX : Measure.map X p = gaussianReal μ v
t : ℝ
⊢ NeZero (gaussianReal μ v)
Module: Mathlib.Probability.Moments.MGFAnalytic

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t : ℝ
inst✝ : IsZeroOrProbabilityMeasure μ
ht : 0 < t
hc : ∫ (x : Ω), X x ∂μ = 0
hs : Set.Icc 0 t ⊆ interior (integrableExpSet X μ)
hu : UniqueDiffOn ℝ (Set.Icc 0 t)
⊢ ∃ u ∈ Set.Ioo 0 t, cgf X μ t - 0 = iteratedDeriv 2 (cgf X μ) u * t ^ 2 / 2

Tactic:
]

Goal state after:
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t : ℝ
inst✝ : IsZeroOrProbabilityMeasure μ
ht : 0 < t
hc : ∫ (x : Ω), X x ∂μ = 0
hs : Set.Icc 0 t ⊆ interior (integrableExpSet X μ)
hu : UniqueDiffOn ℝ (Set.Icc 0 t)
⊢ ∃ u ∈ Set.Ioo 0 t, cgf X μ t - 0 = iteratedDeriv 2 (cgf X μ) u * t ^ 2 / 2
Module: Mathlib.Probability.Moments.MGFAnalytic

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t : ℝ
inst✝ : IsZeroOrProbabilityMeasure μ
ht : 0 < t
hc : ∫ (x : Ω), X x ∂μ = 0
hs : Set.Icc 0 t ⊆ interior (integrableExpSet X μ)
hu : UniqueDiffOn ℝ (Set.Icc 0 t)
⊢ ∃ u ∈ Set.Ioo 0 t, cgf X μ t - 0 = iteratedDeriv 2 (cgf X μ) u * (t - 0) ^ 2 / 2

Tactic:
]

Goal state after:
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t : ℝ
inst✝ : IsZeroOrProbabilityMeasure μ
ht : 0 < t
hc : ∫ (x : Ω), X x ∂μ = 0
hs : Set.Icc 0 t ⊆ interior (integrableExpSet X μ)
hu : UniqueDiffOn ℝ (Set.Icc 0 t)
⊢ ∃ u ∈ Set.Ioo 0 t, cgf X μ t - 0 = iteratedDeriv 2 (cgf X μ) u * (t - 0) ^ 2 / 2
Module: Mathlib.Probability.Moments.MGFAnalytic

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t : ℝ
inst✝ : IsZeroOrProbabilityMeasure μ
ht : 0 < t
hc : ∫ (x : Ω), X x ∂μ = 0
hs : Set.Icc 0 t ⊆ interior (integrableExpSet X μ)
hu : UniqueDiffOn ℝ (Set.Icc 0 t)
⊢ ∃ u ∈ Set.uIoo 0 t, cgf X μ t - 0 = iteratedDeriv 2 (cgf X μ) u * (t - 0) ^ 2 / 2

Tactic:
]

Goal state after:
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t : ℝ
inst✝ : IsZeroOrProbabilityMeasure μ
ht : 0 < t
hc : ∫ (x : Ω), X x ∂μ = 0
hs : Set.Icc 0 t ⊆ interior (integrableExpSet X μ)
hu : UniqueDiffOn ℝ (Set.Icc 0 t)
⊢ ∃ u ∈ Set.uIoo 0 t, cgf X μ t - 0 = iteratedDeriv 2 (cgf X μ) u * (t - 0) ^ 2 / 2
Module: Mathlib.Probability.Distributions.Gaussian.Basic

Goal state before:
pp: E : Type u_1
inst✝³ : TopologicalSpace E
inst✝² : AddCommMonoid E
inst✝¹ : Module ℝ E
mE : MeasurableSpace E
inst✝ : OpensMeasurableSpace E
μ : Measure E
h✝ : ∀ (L : E →L[ℝ] ℝ), ∃ m v, Measure.map (⇑L) μ = gaussianReal m v
L : E →L[ℝ] ℝ
m : ℝ
v : ℝ≥0
h : Measure.map (⇑L) μ = gaussianReal m v
⊢ IsGaussian (gaussianReal m v)

Tactic:
]

Goal state after:
E : Type u_1
inst✝³ : TopologicalSpace E
inst✝² : AddCommMonoid E
inst✝¹ : Module ℝ E
mE : MeasurableSpace E
inst✝ : OpensMeasurableSpace E
μ : Measure E
h✝ : ∀ (L : E →L[ℝ] ℝ), ∃ m v, Measure.map (⇑L) μ = gaussianReal m v
L : E →L[ℝ] ℝ
m : ℝ
v : ℝ≥0
h : Measure.map (⇑L) μ = gaussianReal m v
⊢ IsGaussian (gaussianReal m v)
Module: Mathlib.Probability.Moments.MGFAnalytic

Goal state before:
pp: case convert_3
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t : ℝ
inst✝ : IsZeroOrProbabilityMeasure μ
ht : 0 < t
hc : ∫ (x : Ω), X x ∂μ = 0
hs : Set.Icc 0 t ⊆ interior (integrableExpSet X μ)
hu : UniqueDiffOn ℝ (Set.Icc 0 t)
⊢ ContDiffOn ℝ (↑1 + 1) (cgf X μ) (Set.Icc 0 t)

Tactic:
]

Goal state after:
case convert_3
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
t : ℝ
inst✝ : IsZeroOrProbabilityMeasure μ
ht : 0 < t
hc : ∫ (x : Ω), X x ∂μ = 0
hs : Set.Icc 0 t ⊆ interior (integrableExpSet X μ)
hu : UniqueDiffOn ℝ (Set.Icc 0 t)
⊢ ContDiffOn ℝ (↑1 + 1) (cgf X μ) (Set.Icc 0 t)
Module: Mathlib.Probability.Distributions.Gaussian.Real

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

Tactic:
]

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

Goal state before:
pp: Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
Ω' : Type u_3
mΩ' : MeasurableSpace Ω'
Y : Ω' → ℝ
μ' : Measure Ω'
inst✝¹ : IsFiniteMeasure μ
inst✝ : IsFiniteMeasure μ'
hX : AEMeasurable X μ
hY : AEMeasurable Y μ'
inner_ne_zero : ∀ (x : ℝ), x ≠ 0 → (innerₗ ℝ) x ≠ 0
w : ℝ
h :
  ∫ (ω : Ω), cexp (↑(Multiplicative.toAdd w) * ↑(X ω) * I) ∂μ =
    ∫ (ω : Ω'), cexp (↑(Multiplicative.toAdd w) * ↑(Y ω) * I) ∂μ'
⊢ ∫ (x : Ω), cexp (↑w * ↑(X x) * I) ∂μ = ∫ (x : Ω'), cexp (↑w * ↑(Y x) * I) ∂μ'

Tactic:
]

Goal state after:
Ω : Type u_1
m : MeasurableSpace Ω
X : Ω → ℝ
μ : Measure Ω
Ω' : Type u_3
mΩ' : MeasurableSpace Ω'
Y : Ω' → ℝ
μ' : Measure Ω'
inst✝¹ : IsFiniteMeasure μ
inst✝ : IsFiniteMeasure μ'
hX : AEMeasurable X μ
hY : AEMeasurable Y μ'
inner_ne_zero : ∀ (x : ℝ), x ≠ 0 → (innerₗ ℝ) x ≠ 0
w : ℝ
h :
  ∫ (ω : Ω), cexp (↑(Multiplicative.toAdd w) * ↑(X ω) * I) ∂μ =
    ∫ (ω : Ω'), cexp (↑(Multiplicative.toAdd w) * ↑(Y ω) * I) ∂μ'
⊢ ∫ (x : Ω), cexp (↑w * ↑(X x) * I) ∂μ = ∫ (x : Ω'), cexp (↑w * ↑(Y x) * I) ∂μ'
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: μ : ℝ
v : ℝ≥0
t : ℝ
⊢ rexp (?m.62 * t + ↑?m.63 * t ^ 2 / 2) = rexp (μ * t + ↑v * t ^ 2 / 2)

pp: case hX
μ : ℝ
v : ℝ≥0
t : ℝ
⊢ Measure.map (fun x ↦ x) (gaussianReal μ v) = gaussianReal ?m.62 ?m.63

pp: μ : ℝ
v : ℝ≥0
t : ℝ
⊢ ℝ

pp: μ : ℝ
v : ℝ≥0
t : ℝ
⊢ ℝ≥0

Tactic:
]

Goal state after:
case hX
μ : ℝ
v : ℝ≥0
t : ℝ
⊢ Measure.map (fun x ↦ x) (gaussianReal μ v) = gaussianReal μ v
Module: Mathlib.Probability.Distributions.Gaussian.Real

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

Tactic:
]

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

Goal state before:
pp: μ : ℝ
v : ℝ≥0
t : ℝ
⊢ 0 < rexp (μ * t + ↑v * t ^ 2 / 2)

Tactic:
]

Goal state after:
μ : ℝ
v : ℝ≥0
t : ℝ
⊢ 0 < rexp (μ * t + ↑v * t ^ 2 / 2)
Module: Mathlib.Probability.Distributions.Gaussian.Basic

Goal state before:
pp: E : Type u_1
F : Type u_2
inst✝⁷ : TopologicalSpace E
inst✝⁶ : AddCommMonoid E
inst✝⁵ : Module ℝ E
mE : MeasurableSpace E
inst✝⁴ : TopologicalSpace F
inst✝³ : AddCommMonoid F
inst✝² : Module ℝ F
mF : MeasurableSpace F
inst✝¹ : OpensMeasurableSpace F
μ : Measure E
L : E →L[ℝ] F
inst✝ : IsGaussian μ
hL : Measurable ⇑L
L' : F →L[ℝ] ℝ
⊢ gaussianReal (∫ (x : E), (L' ∘SL L) x ∂μ) Var[⇑(L' ∘SL L); μ].toNNReal =
    gaussianReal (∫ (x : E), (L' ∘SL L) x ∂μ) Var[⇑(L' ∘SL L); μ].toNNReal

Tactic:
]

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

Goal state before:
pp: E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : InnerProductSpace ℝ E
inst✝¹ : MeasurableSpace E
inst✝ : BorelSpace E
⊢ (covarianceBilinDual 0).bilinearComp (toDualMap ℝ E).toContinuousLinearMap (toDualMap ℝ E).toContinuousLinearMap = 0

Tactic:
]

Goal state after:
E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : InnerProductSpace ℝ E
inst✝¹ : MeasurableSpace E
inst✝ : BorelSpace E
⊢ (covarianceBilinDual 0).bilinearComp (toDualMap ℝ E).toContinuousLinearMap (toDualMap ℝ E).toContinuousLinearMap = 0
Module: Mathlib.Probability.Distributions.Gaussian.Real

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

Tactic:
]

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

Goal state before:
pp: E : Type u_1
inst✝³ : NormedAddCommGroup E
inst✝² : InnerProductSpace ℝ E
inst✝¹ : MeasurableSpace E
inst✝ : BorelSpace E
μ : Measure E
x y : E
⊢ ((covarianceBilinDual μ) ((toDualMap ℝ E) y)) ((toDualMap ℝ E) x) =
    ((covarianceBilinDual μ) ((toDualMap ℝ E) y)) ((toDualMap ℝ E) x)

Tactic:
]

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

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

Tactic:
]

Goal state after:
μ : ℝ
v : ℝ≥0
⊢ deriv (fun y ↦ μ) 0 * 0 + μ * deriv (fun y ↦ y) 0 + deriv (fun t ↦ ↑v * t ^ 2 / 2) 0 = μ
Module: Mathlib.Probability.Moments.CovarianceBilin

Goal state before:
pp: E : Type u_1
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : InnerProductSpace ℝ E
inst✝³ : MeasurableSpace E
inst✝² : BorelSpace E
μ : Measure E
inst✝¹ : CompleteSpace E
inst✝ : IsFiniteMeasure μ
h : MemLp id 2 μ
x : E
⊢ Var[⇑((toDualMap ℝ E) x); μ] = Var[fun u ↦ ⟪x, u⟫; μ]

Tactic:
]

Goal state after:
E : Type u_1
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : InnerProductSpace ℝ E
inst✝³ : MeasurableSpace E
inst✝² : BorelSpace E
μ : Measure E
inst✝¹ : CompleteSpace E
inst✝ : IsFiniteMeasure μ
h : MemLp id 2 μ
x : E
⊢ Var[⇑((toDualMap ℝ E) x); μ] = Var[fun u ↦ ⟪x, u⟫; μ]
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: μ : ℝ
v : ℝ≥0
⊢ ∫ (ω : ℝ), (ω - ∫ (x : ℝ), x ∂gaussianReal μ v) ^ 2 ∂gaussianReal μ v = ↑v

Tactic:
]

Goal state after:
μ : ℝ
v : ℝ≥0
⊢ ∫ (ω : ℝ), (ω - ∫ (x : ℝ), x ∂gaussianReal μ v) ^ 2 ∂gaussianReal μ v = ↑v
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: μ : ℝ
v : ℝ≥0
⊢ ∫ (ω : ℝ), (ω - μ) ^ 2 ∂gaussianReal μ v = ∫ (x : ℝ), (x - μ) ^ 2 ∂gaussianReal μ v

Tactic:
]

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

Goal state before:
pp: E : Type u_1
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : InnerProductSpace ℝ E
inst✝³ : MeasurableSpace E
inst✝² : BorelSpace E
μ : Measure E
inst✝¹ : CompleteSpace E
inst✝ : IsFiniteMeasure μ
h : MemLp id 2 μ
x y : E
⊢ cov[⇑((toDualMap ℝ E) x), ⇑((toDualMap ℝ E) y); μ] = cov[fun u ↦ ⟪x, u⟫, fun u ↦ ⟪y, u⟫; μ]

Tactic:
]

Goal state after:
E : Type u_1
inst✝⁵ : NormedAddCommGroup E
inst✝⁴ : InnerProductSpace ℝ E
inst✝³ : MeasurableSpace E
inst✝² : BorelSpace E
μ : Measure E
inst✝¹ : CompleteSpace E
inst✝ : IsFiniteMeasure μ
h : MemLp id 2 μ
x y : E
⊢ cov[⇑((toDualMap ℝ E) x), ⇑((toDualMap ℝ E) y); μ] = cov[fun u ↦ ⟪x, u⟫, fun u ↦ ⟪y, u⟫; μ]
Module: Mathlib.Probability.Distributions.Gaussian.Basic

Goal state before:
pp: E : Type u_1
inst✝⁴ : NormedAddCommGroup E
inst✝³ : NormedSpace ℝ E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsGaussian μ
L : StrongDual ℝ E
p : ℝ≥0∞
hp : p ≠ ∞
⊢ MemLp id p (gaussianReal (∫ (x : E), L x ∂μ) Var[⇑L; μ].toNNReal)

Tactic:
]

Goal state after:
E : Type u_1
inst✝⁴ : NormedAddCommGroup E
inst✝³ : NormedSpace ℝ E
inst✝² : MeasurableSpace E
inst✝¹ : BorelSpace E
μ : Measure E
inst✝ : IsGaussian μ
L : StrongDual ℝ E
p : ℝ≥0∞
hp : p ≠ ∞
⊢ MemLp id p (gaussianReal (∫ (x : E), L x ∂μ) Var[⇑L; μ].toNNReal)
Module: Mathlib.Probability.Distributions.Gaussian.Real

Goal state before:
pp: μ : ℝ
v : ℝ≥0
⊢ ∫ (ω : ℝ), ω ^ 2 ∂gaussianReal 0 v = ∫ (x : ℝ), ((fun x ↦ x) ^ 2) x ∂gaussianReal 0 v

pp: case h
μ : ℝ
v : ℝ≥0
⊢ 0 ∈ interior (integrableExpSet (fun x ↦ x) (gaussianReal 0 v))

Tactic:
]

Goal state after:
μ : ℝ
v : ℝ≥0
⊢ ∫ (ω : ℝ), ω ^ 2 ∂gaussianReal 0 v = ∫ (x : ℝ), ((fun x ↦ x) ^ 2) x ∂gaussianReal 0 v

case h
μ : ℝ
v : ℝ≥0
⊢ 0 ∈ interior (integrableExpSet (fun x ↦ x) (gaussianReal 0 v))
Module: Mathlib.Probability.Distributions.Gaussian.Real

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

Tactic:
]

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