Documentation

BrownianMotion.Auxiliary.MeasureTheory

Measure theory lemmas to be upstreamed to Mathlib #

theorem Filter.EventuallyEq.div' {α : Type u_1} {β : Type u_2} [Div β] {f f' g g' : αβ} {l : Filter α} (h : f =ᶠ[l] g) (h' : f' =ᶠ[l] g') :
f / f' =ᶠ[l] g / g'
theorem Filter.EventuallyEq.sub' {α : Type u_1} {β : Type u_2} [Sub β] {f f' g g' : αβ} {l : Filter α} (h : f =ᶠ[l] g) (h' : f' =ᶠ[l] g') :
f - f' =ᶠ[l] g - g'
theorem ProbabilityTheory.charFun_pi {ι : Type u_1} [Fintype ι] {E : ιType u_2} {mE : (i : ι) → MeasurableSpace (E i)} [(i : ι) → NormedAddCommGroup (E i)] [(i : ι) → InnerProductSpace (E i)] (μ : (i : ι) → MeasureTheory.Measure (E i)) [∀ (i : ι), MeasureTheory.IsProbabilityMeasure (μ i)] (t : PiLp 2 E) :
theorem ProbabilityTheory.variance_pi {ι : Type u_1} [Fintype ι] {Ω : ιType u_2} { : (i : ι) → MeasurableSpace (Ω i)} {μ : (i : ι) → MeasureTheory.Measure (Ω i)} [∀ (i : ι), MeasureTheory.IsProbabilityMeasure (μ i)] {X : (i : ι) → Ω i} (h : ∀ (i : ι), MeasureTheory.MemLp (X i) 2 (μ i)) :
variance (∑ i : ι, fun (ω : (i : ι) → Ω i) => X i (ω i)) (MeasureTheory.Measure.pi μ) = i : ι, variance (X i) (μ i)
theorem ProbabilityTheory.centralMoment_of_integral_id_eq_zero {Ω : Type u_1} { : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X : Ω} (p : ) (hX : (x : Ω), X x μ = 0) :
centralMoment X p μ = (ω : Ω), X ω ^ p μ
theorem ContinuousLinearMap.integral_comp_id_comm' {𝕜 : Type u_1} {E : Type u_2} {F : Type u_3} [RCLike 𝕜] [NormedAddCommGroup E] [NormedAddCommGroup F] [NormedSpace 𝕜 E] [NormedSpace E] [NormedSpace 𝕜 F] [NormedSpace F] [CompleteSpace E] [CompleteSpace F] [MeasurableSpace E] {μ : MeasureTheory.Measure E} (h : MeasureTheory.Integrable id μ) (L : E →L[𝕜] F) :
(x : E), L x μ = L ( (x : E), id x μ)
theorem ContinuousLinearMap.integral_comp_id_comm {𝕜 : Type u_1} {E : Type u_2} {F : Type u_3} [RCLike 𝕜] [NormedAddCommGroup E] [NormedAddCommGroup F] [NormedSpace 𝕜 E] [NormedSpace E] [NormedSpace 𝕜 F] [NormedSpace F] [CompleteSpace E] [CompleteSpace F] [MeasurableSpace E] {μ : MeasureTheory.Measure E} (h : MeasureTheory.Integrable id μ) (L : E →L[𝕜] F) :
(x : E), L x μ = L ( (x : E), x μ)
@[simp]
theorem zero_mem_parallelepiped {ι : Type u_1} {E : Type u_2} [Fintype ι] [AddCommGroup E] [Module E] {v : ιE} :
@[simp]
theorem nonempty_parallelepiped {ι : Type u_1} {E : Type u_2} [Fintype ι] [AddCommGroup E] [Module E] {v : ιE} :
theorem ProbabilityTheory.covariance_fun_add_left {Ω : Type u_1} { : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X Y Z : Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (hZ : MeasureTheory.MemLp Z 2 μ) :
covariance (fun (ω : Ω) => X ω + Y ω) Z μ = covariance (fun (ω : Ω) => X ω) Z μ + covariance (fun (ω : Ω) => Y ω) Z μ
theorem ProbabilityTheory.covariance_fun_add_right {Ω : Type u_1} { : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X Y Z : Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (hZ : MeasureTheory.MemLp Z 2 μ) :
covariance X (fun (ω : Ω) => Y ω + Z ω) μ = covariance X (fun (ω : Ω) => Y ω) μ + covariance X (fun (ω : Ω) => Z ω) μ
theorem ProbabilityTheory.covariance_fun_sub_left {Ω : Type u_1} { : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X Y Z : Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (hZ : MeasureTheory.MemLp Z 2 μ) :
covariance (fun (ω : Ω) => X ω - Y ω) Z μ = covariance X Z μ - covariance Y Z μ
theorem ProbabilityTheory.covariance_fun_sub_right {Ω : Type u_1} { : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X Y Z : Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (hZ : MeasureTheory.MemLp Z 2 μ) :
covariance X (fun (ω : Ω) => Y ω - Z ω) μ = covariance X (fun (ω : Ω) => Y ω) μ - covariance X (fun (ω : Ω) => Z ω) μ
theorem ProbabilityTheory.covariance_fun_div_left {Ω : Type u_1} { : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X Y : Ω} (c : ) :
covariance (fun (ω : Ω) => X ω / c) Y μ = covariance X Y μ / c
theorem ProbabilityTheory.covariance_fun_div_right {Ω : Type u_1} { : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X Y : Ω} (c : ) :
covariance X (fun (ω : Ω) => Y ω / c) μ = covariance X Y μ / c
theorem ProbabilityTheory.variance_fun_div {Ω : Type u_1} { : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X : Ω} (c : ) (hX : AEMeasurable X μ) :
variance (fun (ω : Ω) => X ω / c) μ = variance X μ / c ^ 2