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Found 89 declarations mentioning ProbabilityTheory.covariance.
- ProbabilityTheory.covariance 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} (X Y : Ω → ℝ) (μ : MeasureTheory.Measure Ω) : ℝ - ProbabilityTheory.covariance_comm 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} (X Y : Ω → ℝ) {μ : MeasureTheory.Measure Ω} : ProbabilityTheory.covariance X Y μ = ProbabilityTheory.covariance Y X μ - ProbabilityTheory.covariance_const_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (c : ℝ) : ProbabilityTheory.covariance (fun x => c) Y μ = 0 - ProbabilityTheory.covariance_const_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (c : ℝ) : ProbabilityTheory.covariance X (fun x => c) μ = 0 - ProbabilityTheory.covariance_fun_neg_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} : ProbabilityTheory.covariance (fun ω => -X ω) Y μ = -ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_fun_neg_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} : ProbabilityTheory.covariance X (fun ω => -Y ω) μ = -ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_zero_measure 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} : ProbabilityTheory.covariance X Y 0 = 0 - ProbabilityTheory.covariance_zero_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} : ProbabilityTheory.covariance 0 Y μ = 0 - ProbabilityTheory.covariance_zero_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} : ProbabilityTheory.covariance X 0 μ = 0 - ProbabilityTheory.covariance_neg_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} : ProbabilityTheory.covariance (-X) Y μ = -ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_neg_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} : ProbabilityTheory.covariance X (-Y) μ = -ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_const_mul_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (c : ℝ) : ProbabilityTheory.covariance (fun ω => c * X ω) Y μ = c * ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_const_mul_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (c : ℝ) : ProbabilityTheory.covariance X (fun ω => c * Y ω) μ = c * ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_mul_const_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (c : ℝ) : ProbabilityTheory.covariance (fun ω => X ω * c) Y μ = ProbabilityTheory.covariance X Y μ * c - ProbabilityTheory.covariance_mul_const_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (c : ℝ) : ProbabilityTheory.covariance X (fun ω => Y ω * c) μ = ProbabilityTheory.covariance X Y μ * c - ProbabilityTheory.covariance_fun_div_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (c : ℝ) : ProbabilityTheory.covariance (fun ω => X ω / c) Y μ = ProbabilityTheory.covariance X Y μ / c - ProbabilityTheory.covariance_fun_div_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (c : ℝ) : ProbabilityTheory.covariance X (fun ω => Y ω / c) μ = ProbabilityTheory.covariance X Y μ / c - ProbabilityTheory.covariance_smul_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (c : ℝ) : ProbabilityTheory.covariance (c • X) Y μ = c * ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_smul_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (c : ℝ) : ProbabilityTheory.covariance X (c • Y) μ = c * ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_add_const_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hX : MeasureTheory.Integrable X μ) (c : ℝ) : ProbabilityTheory.covariance (fun ω => X ω + c) Y μ = ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_add_const_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hY : MeasureTheory.Integrable Y μ) (c : ℝ) : ProbabilityTheory.covariance X (fun ω => Y ω + c) μ = ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_const_add_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hX : MeasureTheory.Integrable X μ) (c : ℝ) : ProbabilityTheory.covariance (fun ω => c + X ω) Y μ = ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_const_add_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hY : MeasureTheory.Integrable Y μ) (c : ℝ) : ProbabilityTheory.covariance X (fun ω => c + Y ω) μ = ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_sub_const_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hX : MeasureTheory.Integrable X μ) (c : ℝ) : ProbabilityTheory.covariance (fun ω => X ω - c) Y μ = ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_sub_const_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hY : MeasureTheory.Integrable Y μ) (c : ℝ) : ProbabilityTheory.covariance X (fun ω => Y ω - c) μ = ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_const_sub_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hX : MeasureTheory.Integrable X μ) (c : ℝ) : ProbabilityTheory.covariance (fun ω => c - X ω) Y μ = -ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_const_sub_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hY : MeasureTheory.Integrable Y μ) (c : ℝ) : ProbabilityTheory.covariance X (fun ω => c - Y ω) μ = -ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance_map_fun 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {Ω' : Type u_2} {mΩ' : MeasurableSpace Ω'} {μ : MeasureTheory.Measure Ω'} {Z : Ω' → Ω} (hX : MeasureTheory.AEStronglyMeasurable X (MeasureTheory.Measure.map Z μ)) (hY : MeasureTheory.AEStronglyMeasurable Y (MeasureTheory.Measure.map Z μ)) (hZ : AEMeasurable Z μ) : ProbabilityTheory.covariance X Y (MeasureTheory.Measure.map Z μ) = ProbabilityTheory.covariance (fun ω => X (Z ω)) (fun ω => Y (Z ω)) μ - ProbabilityTheory.covariance_map 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {Ω' : Type u_2} {mΩ' : MeasurableSpace Ω'} {μ : MeasureTheory.Measure Ω'} {Z : Ω' → Ω} (hX : MeasureTheory.AEStronglyMeasurable X (MeasureTheory.Measure.map Z μ)) (hY : MeasureTheory.AEStronglyMeasurable Y (MeasureTheory.Measure.map Z μ)) (hZ : AEMeasurable Z μ) : ProbabilityTheory.covariance X Y (MeasureTheory.Measure.map Z μ) = ProbabilityTheory.covariance (X ∘ Z) (Y ∘ Z) μ - ProbabilityTheory.covariance_map_equiv 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {Ω' : Type u_2} {mΩ' : MeasurableSpace Ω'} {μ : MeasureTheory.Measure Ω'} (X Y : Ω → ℝ) (Z : Ω' ≃ᵐ Ω) : ProbabilityTheory.covariance X Y (MeasureTheory.Measure.map (⇑Z) μ) = ProbabilityTheory.covariance (X ∘ ⇑Z) (Y ∘ ⇑Z) μ - ProbabilityTheory.IndepFun.covariance_eq_zero 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (h : ProbabilityTheory.IndepFun X Y μ) (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.covariance X Y μ = 0 - ProbabilityTheory.covariance_fun_sum_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} [MeasureTheory.IsFiniteMeasure μ] [Fintype ι] (hX : ∀ (i : ι), MeasureTheory.MemLp (X i) 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.covariance (fun ω => ∑ i, X i ω) Y μ = ∑ i, ProbabilityTheory.covariance (X i) Y μ - ProbabilityTheory.covariance_fun_sum_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} [MeasureTheory.IsFiniteMeasure μ] [Fintype ι] (hX : ∀ (i : ι), MeasureTheory.MemLp (X i) 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.covariance Y (fun ω => ∑ i, X i ω) μ = ∑ i, ProbabilityTheory.covariance Y (X i) μ - ProbabilityTheory.covariance_sum_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} [MeasureTheory.IsFiniteMeasure μ] [Fintype ι] (hX : ∀ (i : ι), MeasureTheory.MemLp (X i) 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.covariance (∑ i, X i) Y μ = ∑ i, ProbabilityTheory.covariance (X i) Y μ - ProbabilityTheory.covariance_sum_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} [MeasureTheory.IsFiniteMeasure μ] [Fintype ι] (hX : ∀ (i : ι), MeasureTheory.MemLp (X i) 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.covariance Y (∑ i, X i) μ = ∑ i, ProbabilityTheory.covariance Y (X i) μ - ProbabilityTheory.covariance_fun_sum_left' 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} {s : Finset ι} [MeasureTheory.IsFiniteMeasure μ] (hX : ∀ i ∈ s, MeasureTheory.MemLp (X i) 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.covariance (fun ω => ∑ i ∈ s, X i ω) Y μ = ∑ i ∈ s, ProbabilityTheory.covariance (X i) Y μ - ProbabilityTheory.covariance_fun_sum_right' 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} {s : Finset ι} [MeasureTheory.IsFiniteMeasure μ] (hX : ∀ i ∈ s, MeasureTheory.MemLp (X i) 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.covariance Y (fun ω => ∑ i ∈ s, X i ω) μ = ∑ i ∈ s, ProbabilityTheory.covariance Y (X i) μ - ProbabilityTheory.covariance_fst_snd_prod 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {Ω' : Type u_2} {mΩ' : MeasurableSpace Ω'} {ν : MeasureTheory.Measure Ω'} [MeasureTheory.IsProbabilityMeasure μ] [MeasureTheory.IsProbabilityMeasure ν] {X : Ω → ℝ} {Y : Ω' → ℝ} (hfμ : MeasureTheory.MemLp X 2 μ) (hgν : MeasureTheory.MemLp Y 2 ν) : ProbabilityTheory.covariance (fun p => X p.1) (fun p => Y p.2) (μ.prod ν) = 0 - ProbabilityTheory.covariance_sum_left' 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} {s : Finset ι} [MeasureTheory.IsFiniteMeasure μ] (hX : ∀ i ∈ s, MeasureTheory.MemLp (X i) 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.covariance (∑ i ∈ s, X i) Y μ = ∑ i ∈ s, ProbabilityTheory.covariance (X i) Y μ - ProbabilityTheory.covariance_sum_right' 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} {s : Finset ι} [MeasureTheory.IsFiniteMeasure μ] (hX : ∀ i ∈ s, MeasureTheory.MemLp (X i) 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.covariance Y (∑ i ∈ s, X i) μ = ∑ i ∈ s, ProbabilityTheory.covariance Y (X i) μ - ProbabilityTheory.covariance_fun_sum_fun_sum 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} [MeasureTheory.IsFiniteMeasure μ] [Fintype ι] {ι' : Type u_3} [Fintype ι'] {Y : ι' → Ω → ℝ} (hX : ∀ (i : ι), MeasureTheory.MemLp (X i) 2 μ) (hY : ∀ (i : ι'), MeasureTheory.MemLp (Y i) 2 μ) : ProbabilityTheory.covariance (fun ω => ∑ i, X i ω) (fun ω => ∑ j, Y j ω) μ = ∑ i, ∑ j, ProbabilityTheory.covariance (X i) (Y j) μ - ProbabilityTheory.covariance_sum_sum 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} [MeasureTheory.IsFiniteMeasure μ] [Fintype ι] {ι' : Type u_3} [Fintype ι'] {Y : ι' → Ω → ℝ} (hX : ∀ (i : ι), MeasureTheory.MemLp (X i) 2 μ) (hY : ∀ (i : ι'), MeasureTheory.MemLp (Y i) 2 μ) : ProbabilityTheory.covariance (∑ i, X i) (∑ j, Y j) μ = ∑ i, ∑ j, ProbabilityTheory.covariance (X i) (Y j) μ - ProbabilityTheory.covariance_fun_sum_fun_sum' 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} {s : Finset ι} [MeasureTheory.IsFiniteMeasure μ] {ι' : Type u_3} {Y : ι' → Ω → ℝ} {t : Finset ι'} (hX : ∀ i ∈ s, MeasureTheory.MemLp (X i) 2 μ) (hY : ∀ i ∈ t, MeasureTheory.MemLp (Y i) 2 μ) : ProbabilityTheory.covariance (fun ω => ∑ i ∈ s, X i ω) (fun ω => ∑ j ∈ t, Y j ω) μ = ∑ i ∈ s, ∑ j ∈ t, ProbabilityTheory.covariance (X i) (Y j) μ - ProbabilityTheory.covariance_sum_sum' 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} {s : Finset ι} [MeasureTheory.IsFiniteMeasure μ] {ι' : Type u_3} {Y : ι' → Ω → ℝ} {t : Finset ι'} (hX : ∀ i ∈ s, MeasureTheory.MemLp (X i) 2 μ) (hY : ∀ i ∈ t, MeasureTheory.MemLp (Y i) 2 μ) : ProbabilityTheory.covariance (∑ i ∈ s, X i) (∑ j ∈ t, Y j) μ = ∑ i ∈ s, ∑ j ∈ t, ProbabilityTheory.covariance (X i) (Y j) μ - ProbabilityTheory.covariance_eq_sub 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.covariance X Y μ = ∫ (x : Ω), (X * Y) x ∂μ - (∫ (x : Ω), X x ∂μ) * ∫ (x : Ω), Y x ∂μ - ProbabilityTheory.covariance_fun_sub_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y Z : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (hZ : MeasureTheory.MemLp Z 2 μ) : ProbabilityTheory.covariance (fun ω => X ω - Y ω) Z μ = ProbabilityTheory.covariance X Z μ - ProbabilityTheory.covariance Y Z μ - ProbabilityTheory.covariance_fun_sub_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y Z : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (hZ : MeasureTheory.MemLp Z 2 μ) : ProbabilityTheory.covariance X (fun ω => Y ω - Z ω) μ = ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance X Z μ - ProbabilityTheory.covariance_add_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y Z : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (hZ : MeasureTheory.MemLp Z 2 μ) : ProbabilityTheory.covariance (X + Y) Z μ = ProbabilityTheory.covariance X Z μ + ProbabilityTheory.covariance Y Z μ - ProbabilityTheory.covariance_add_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y Z : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (hZ : MeasureTheory.MemLp Z 2 μ) : ProbabilityTheory.covariance X (Y + Z) μ = ProbabilityTheory.covariance X Y μ + ProbabilityTheory.covariance X Z μ - ProbabilityTheory.covariance_sub_left 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y Z : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (hZ : MeasureTheory.MemLp Z 2 μ) : ProbabilityTheory.covariance (X - Y) Z μ = ProbabilityTheory.covariance X Z μ - ProbabilityTheory.covariance Y Z μ - ProbabilityTheory.covariance_sub_right 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y Z : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (hZ : MeasureTheory.MemLp Z 2 μ) : ProbabilityTheory.covariance X (Y - Z) μ = ProbabilityTheory.covariance X Y μ - ProbabilityTheory.covariance X Z μ - ProbabilityTheory.covariance_fun_sub_fun_sub 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y Z T : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (hZ : MeasureTheory.MemLp Z 2 μ) (hT : MeasureTheory.MemLp T 2 μ) : ProbabilityTheory.covariance (fun ω => X ω - Y ω) (fun ω => Z ω - T ω) μ = ProbabilityTheory.covariance X Z μ - ProbabilityTheory.covariance X T μ - ProbabilityTheory.covariance Y Z μ + ProbabilityTheory.covariance Y T μ - ProbabilityTheory.covariance_sub_sub 📋 Mathlib.Probability.Moments.Covariance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y Z T : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (hZ : MeasureTheory.MemLp Z 2 μ) (hT : MeasureTheory.MemLp T 2 μ) : ProbabilityTheory.covariance (X - Y) (Z - T) μ = ProbabilityTheory.covariance X Z μ - ProbabilityTheory.covariance X T μ - ProbabilityTheory.covariance Y Z μ + ProbabilityTheory.covariance Y T μ - ProbabilityTheory.covariance_self 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X : Ω → ℝ} (hX : AEMeasurable X μ) : ProbabilityTheory.covariance X X μ = ProbabilityTheory.variance X μ - ProbabilityTheory.variance_fun_sum 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} [MeasureTheory.IsFiniteMeasure μ] [Fintype ι] (hX : ∀ (i : ι), MeasureTheory.MemLp (X i) 2 μ) : ProbabilityTheory.variance (fun ω => ∑ i, X i ω) μ = ∑ i, ∑ j, ProbabilityTheory.covariance (X i) (X j) μ - ProbabilityTheory.variance_sum 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {X : ι → Ω → ℝ} [MeasureTheory.IsFiniteMeasure μ] [Fintype ι] (hX : ∀ (i : ι), MeasureTheory.MemLp (X i) 2 μ) : ProbabilityTheory.variance (∑ i, X i) μ = ∑ i, ∑ j, ProbabilityTheory.covariance (X i) (X j) μ - ProbabilityTheory.variance_fun_sum' 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {s : Finset ι} {X : ι → Ω → ℝ} [MeasureTheory.IsFiniteMeasure μ] (hX : ∀ i ∈ s, MeasureTheory.MemLp (X i) 2 μ) : ProbabilityTheory.variance (fun ω => ∑ i ∈ s, X i ω) μ = ∑ i ∈ s, ∑ j ∈ s, ProbabilityTheory.covariance (X i) (X j) μ - ProbabilityTheory.variance_sum' 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {ι : Type u_2} {s : Finset ι} {X : ι → Ω → ℝ} [MeasureTheory.IsFiniteMeasure μ] (hX : ∀ i ∈ s, MeasureTheory.MemLp (X i) 2 μ) : ProbabilityTheory.variance (∑ i ∈ s, X i) μ = ∑ i ∈ s, ∑ j ∈ s, ProbabilityTheory.covariance (X i) (X j) μ - ProbabilityTheory.variance_fun_add 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.variance (fun ω => X ω + Y ω) μ = ProbabilityTheory.variance X μ + 2 * ProbabilityTheory.covariance X Y μ + ProbabilityTheory.variance Y μ - ProbabilityTheory.variance_fun_sub 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.variance (fun ω => X ω - Y ω) μ = ProbabilityTheory.variance X μ - 2 * ProbabilityTheory.covariance X Y μ + ProbabilityTheory.variance Y μ - ProbabilityTheory.variance_add 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.variance (X + Y) μ = ProbabilityTheory.variance X μ + 2 * ProbabilityTheory.covariance X Y μ + ProbabilityTheory.variance Y μ - ProbabilityTheory.variance_sub 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) : ProbabilityTheory.variance (X - Y) μ = ProbabilityTheory.variance X μ - 2 * ProbabilityTheory.covariance X Y μ + ProbabilityTheory.variance Y μ - ProbabilityTheory.HasLaw.covariance_fun_comp 📋 Mathlib.Probability.HasLaw
{Ω : Type u_1} {𝓧 : Type u_2} {mΩ : MeasurableSpace Ω} {m𝓧 : MeasurableSpace 𝓧} {X : Ω → 𝓧} {μ : MeasureTheory.Measure 𝓧} {P : MeasureTheory.Measure Ω} (hX : ProbabilityTheory.HasLaw X μ P) {f g : 𝓧 → ℝ} (hf : AEMeasurable f μ) (hg : AEMeasurable g μ) : ProbabilityTheory.covariance (fun ω => f (X ω)) (fun ω => g (X ω)) P = ProbabilityTheory.covariance f g μ - ProbabilityTheory.HasLaw.covariance_comp 📋 Mathlib.Probability.HasLaw
{Ω : Type u_1} {𝓧 : Type u_2} {mΩ : MeasurableSpace Ω} {m𝓧 : MeasurableSpace 𝓧} {X : Ω → 𝓧} {μ : MeasureTheory.Measure 𝓧} {P : MeasureTheory.Measure Ω} (hX : ProbabilityTheory.HasLaw X μ P) {f g : 𝓧 → ℝ} (hf : AEMeasurable f μ) (hg : AEMeasurable g μ) : ProbabilityTheory.covariance (f ∘ X) (g ∘ X) P = ProbabilityTheory.covariance f g μ - ProbabilityTheory.covarianceBilinDual_eq_covariance 📋 Mathlib.Probability.Moments.CovarianceBilinDual
{E : Type u_1} [NormedAddCommGroup E] {mE : MeasurableSpace E} {μ : MeasureTheory.Measure E} [NormedSpace ℝ E] [BorelSpace E] [CompleteSpace E] [MeasureTheory.IsFiniteMeasure μ] (h : MeasureTheory.MemLp id 2 μ) (L₁ L₂ : StrongDual ℝ E) : ((ProbabilityTheory.covarianceBilinDual μ) L₁) L₂ = ProbabilityTheory.covariance (⇑L₁) (⇑L₂) μ - ProbabilityTheory.covarianceBilin_apply_eq_cov 📋 Mathlib.Probability.Moments.CovarianceBilin
{E : Type u_1} [NormedAddCommGroup E] [InnerProductSpace ℝ E] [MeasurableSpace E] [BorelSpace E] {μ : MeasureTheory.Measure E} [CompleteSpace E] [MeasureTheory.IsFiniteMeasure μ] (h : MeasureTheory.MemLp id 2 μ) (x y : E) : ((ProbabilityTheory.covarianceBilin μ) x) y = ProbabilityTheory.covariance (fun u => inner ℝ x u) (fun u => inner ℝ y u) μ - ProbabilityTheory.covarianceBilin_apply_pi 📋 Mathlib.Probability.Moments.CovarianceBilin
{ι : Type u_2} {Ω : Type u_3} [Fintype ι] {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] {X : ι → Ω → ℝ} (hX : ∀ (i : ι), MeasureTheory.MemLp (X i) 2 μ) (x y : EuclideanSpace ℝ ι) : ((ProbabilityTheory.covarianceBilin (MeasureTheory.Measure.map (fun ω => WithLp.toLp 2 fun x => X x ω) μ)) x) y = ∑ i, ∑ j, x.ofLp i * y.ofLp j * ProbabilityTheory.covariance (X i) (X j) μ - ProbabilityTheory.covarianceBilin_apply_basisFun 📋 Mathlib.Probability.Moments.CovarianceBilin
{ι : Type u_2} {Ω : Type u_3} [Fintype ι] {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] {X : ι → Ω → ℝ} (hX : ∀ (i : ι), MeasureTheory.MemLp (X i) 2 μ) (i j : ι) : ((ProbabilityTheory.covarianceBilin (MeasureTheory.Measure.map (fun ω => WithLp.toLp 2 fun x => X x ω) μ)) ((EuclideanSpace.basisFun ι ℝ) i)) ((EuclideanSpace.basisFun ι ℝ) j) = ProbabilityTheory.covariance (X i) (X j) μ - ProbabilityTheory.covariance_eval_multivariateGaussian 📋 Mathlib.Probability.Distributions.Gaussian.Multivariate
{ι : Type u_1} [Fintype ι] [DecidableEq ι] {μ : EuclideanSpace ℝ ι} {S : Matrix ι ι ℝ} (hS : S.PosSemidef) (i j : ι) : ProbabilityTheory.covariance (fun x => x.ofLp i) (fun x => x.ofLp j) (ProbabilityTheory.multivariateGaussian μ S) = S i j - ProbabilityTheory.BrownianReal.covariance_eval_projectiveFamily 📋 Mathlib.Probability.BrownianMotion.GaussianProjectiveFamily
(I : Finset NNReal) (s t : ↥I) : ProbabilityTheory.covariance (fun x => x s) (fun x => x t) (ProbabilityTheory.BrownianReal.projectiveFamily I) = ↑(min ↑s ↑t) - ProbabilityTheory.BrownianReal.covariance_fun_projectiveFamily 📋 Mathlib.Probability.BrownianMotion.GaussianProjectiveFamily
(I : Finset NNReal) (f g : (↥I → ℝ) → ℝ) : ProbabilityTheory.covariance f g (ProbabilityTheory.BrownianReal.projectiveFamily I) = ProbabilityTheory.covariance (fun x => f x.ofLp) (fun x => g x.ofLp) (ProbabilityTheory.multivariateGaussian 0 (ProbabilityTheory.BrownianReal.covMatrix I)) - ProbabilityTheory.BrownianReal.covariance_projectiveFamily 📋 Mathlib.Probability.BrownianMotion.GaussianProjectiveFamily
(I : Finset NNReal) (f g : (↥I → ℝ) → ℝ) : ProbabilityTheory.covariance f g (ProbabilityTheory.BrownianReal.projectiveFamily I) = ProbabilityTheory.covariance (f ∘ WithLp.ofLp) (g ∘ WithLp.ofLp) (ProbabilityTheory.multivariateGaussian 0 (ProbabilityTheory.BrownianReal.covMatrix I)) - ProbabilityTheory.HasGaussianLaw.indepFun_of_covariance_eq_zero 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X Y : Ω → ℝ} (hXY : ProbabilityTheory.HasGaussianLaw (fun ω => (X ω, Y ω)) P) (h : ProbabilityTheory.covariance X Y P = 0) : ProbabilityTheory.IndepFun X Y P - ProbabilityTheory.HasGaussianLaw.iIndepFun_of_covariance_eq_zero 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {ι : Type u_2} [Finite ι] {X : ι → Ω → ℝ} (hX : ProbabilityTheory.HasGaussianLaw (fun ω x => X x ω) P) (h : ∀ (i j : ι), i ≠ j → ProbabilityTheory.covariance (X i) (X j) P = 0) : ProbabilityTheory.iIndepFun X P - ProbabilityTheory.HasGaussianLaw.iIndepFun_of_covariance_eval 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {ι : Type u_2} [Finite ι] {κ : ι → Type u_4} [∀ (i : ι), Finite (κ i)] {X : (i : ι) → κ i → Ω → ℝ} (hX : ProbabilityTheory.HasGaussianLaw (fun ω i j => X i j ω) P) (h : ∀ (i j : ι), i ≠ j → ∀ (k : κ i) (l : κ j), ProbabilityTheory.covariance (X i k) (X j l) P = 0) : ProbabilityTheory.iIndepFun (fun i ω j => X i j ω) P - ProbabilityTheory.HasGaussianLaw.indepFun_of_covariance_eval 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {ι : Type u_4} {κ : Type u_5} [Finite ι] [Finite κ] {X : ι → Ω → ℝ} {Y : κ → Ω → ℝ} (hXY : ProbabilityTheory.HasGaussianLaw (fun ω => (fun i => X i ω, fun j => Y j ω)) P) (h : ∀ (i : ι) (j : κ), ProbabilityTheory.covariance (X i) (Y j) P = 0) : ProbabilityTheory.IndepFun (fun ω i => X i ω) (fun ω j => Y j ω) P - ProbabilityTheory.HasGaussianLaw.iIndepFun_of_covariance_inner 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {ι : Type u_2} [Finite ι] {E : ι → Type u_3} [(i : ι) → NormedAddCommGroup (E i)] [(i : ι) → MeasurableSpace (E i)] [∀ (i : ι), CompleteSpace (E i)] [∀ (i : ι), BorelSpace (E i)] [∀ (i : ι), SecondCountableTopology (E i)] [(i : ι) → InnerProductSpace ℝ (E i)] {X : (i : ι) → Ω → E i} (hX : ProbabilityTheory.HasGaussianLaw (fun ω i => X i ω) P) (h : ∀ (i j : ι), i ≠ j → ∀ (x : E i) (y : E j), ProbabilityTheory.covariance (fun ω => inner ℝ x (X i ω)) (fun ω => inner ℝ y (X j ω)) P = 0) : ProbabilityTheory.iIndepFun X P - ProbabilityTheory.HasGaussianLaw.indepFun_of_covariance_inner 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {E : Type u_2} {F : Type u_3} [NormedAddCommGroup E] [MeasurableSpace E] [CompleteSpace E] [BorelSpace E] [SecondCountableTopology E] [NormedAddCommGroup F] [MeasurableSpace F] [CompleteSpace F] [BorelSpace F] [SecondCountableTopology F] [InnerProductSpace ℝ E] [InnerProductSpace ℝ F] {X : Ω → E} {Y : Ω → F} (hXY : ProbabilityTheory.HasGaussianLaw (fun ω => (X ω, Y ω)) P) (h : ∀ (x : E) (y : F), ProbabilityTheory.covariance (fun ω => inner ℝ x (X ω)) (fun ω => inner ℝ y (Y ω)) P = 0) : ProbabilityTheory.IndepFun X Y P - ProbabilityTheory.HasGaussianLaw.indepFun_of_covariance_strongDual 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {E : Type u_2} {F : Type u_3} [NormedAddCommGroup E] [MeasurableSpace E] [CompleteSpace E] [BorelSpace E] [SecondCountableTopology E] [NormedAddCommGroup F] [MeasurableSpace F] [CompleteSpace F] [BorelSpace F] [SecondCountableTopology F] [NormedSpace ℝ E] [NormedSpace ℝ F] {X : Ω → E} {Y : Ω → F} (hXY : ProbabilityTheory.HasGaussianLaw (fun ω => (X ω, Y ω)) P) (h : ∀ (L₁ : StrongDual ℝ E) (L₂ : StrongDual ℝ F), ProbabilityTheory.covariance (⇑L₁ ∘ X) (⇑L₂ ∘ Y) P = 0) : ProbabilityTheory.IndepFun X Y P - ProbabilityTheory.HasGaussianLaw.iIndepFun_of_covariance_strongDual 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {ι : Type u_2} [Finite ι] {E : ι → Type u_3} [(i : ι) → NormedAddCommGroup (E i)] [(i : ι) → MeasurableSpace (E i)] [∀ (i : ι), CompleteSpace (E i)] [∀ (i : ι), BorelSpace (E i)] [∀ (i : ι), SecondCountableTopology (E i)] [(i : ι) → NormedSpace ℝ (E i)] {X : (i : ι) → Ω → E i} (hX : ProbabilityTheory.HasGaussianLaw (fun ω i => X i ω) P) (h : ∀ (i j : ι), i ≠ j → ∀ (L₁ : StrongDual ℝ (E i)) (L₂ : StrongDual ℝ (E j)), ProbabilityTheory.covariance (⇑L₁ ∘ X i) (⇑L₂ ∘ X j) P = 0) : ProbabilityTheory.iIndepFun X P - ProbabilityTheory.IsGaussianProcess.indepFun_of_covariance_eq_zero 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Independence
{T : Type u_1} {Ω : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {S : Type u_4} {X : S → Ω → ℝ} {Y : T → Ω → ℝ} (hXY : ProbabilityTheory.IsGaussianProcess (Sum.elim X Y) P) (mX : ∀ (s : S), AEMeasurable (X s) P) (mY : ∀ (t : T), AEMeasurable (Y t) P) (h : ∀ (s : S) (t : T), ProbabilityTheory.covariance (X s) (Y t) P = 0) : ProbabilityTheory.IndepFun (fun ω s => X s ω) (fun ω t => Y t ω) P - ProbabilityTheory.IsGaussianProcess.iIndepFun_of_covariance_eq_zero 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Independence
{T : Type u_1} {Ω : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {S : T → Type u_4} {X : (t : T) → S t → Ω → ℝ} (hX : ProbabilityTheory.IsGaussianProcess (fun p ω => X p.fst p.snd ω) P) (mX : ∀ (t : T) (s : S t), AEMeasurable (X t s) P) (h : ∀ (t₁ t₂ : T), t₁ ≠ t₂ → ∀ (s₁ : S t₁) (s₂ : S t₂), ProbabilityTheory.covariance (X t₁ s₁) (X t₂ s₂) P = 0) : ProbabilityTheory.iIndepFun (fun t ω s => X t s ω) P - ProbabilityTheory.IsGaussianProcess.indepFun_of_covariance_inner 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Independence
{T : Type u_1} {Ω : Type u_2} {E : Type u_3} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] [CompleteSpace E] {S : Type u_4} {X : S → Ω → E} {Y : T → Ω → E} [InnerProductSpace ℝ E] (hXY : ProbabilityTheory.IsGaussianProcess (Sum.elim X Y) P) (mX : ∀ (s : S), AEMeasurable (X s) P) (mY : ∀ (t : T), AEMeasurable (Y t) P) (h : ∀ (s : S) (t : T) (x y : E), ProbabilityTheory.covariance (fun ω => inner ℝ x (X s ω)) (fun ω => inner ℝ y (Y t ω)) P = 0) : ProbabilityTheory.IndepFun (fun ω s => X s ω) (fun ω t => Y t ω) P - ProbabilityTheory.IsGaussianProcess.iIndepFun_of_covariance_inner 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Independence
{T : Type u_1} {Ω : Type u_2} {E : Type u_3} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] [CompleteSpace E] {S : T → Type u_4} {X : (t : T) → S t → Ω → E} [InnerProductSpace ℝ E] (hX : ProbabilityTheory.IsGaussianProcess (fun p ω => X p.fst p.snd ω) P) (mX : ∀ (t : T) (s : S t), AEMeasurable (X t s) P) (h : ∀ (t₁ t₂ : T), t₁ ≠ t₂ → ∀ (s₁ : S t₁) (s₂ : S t₂) (x y : E), ProbabilityTheory.covariance (fun ω => inner ℝ x (X t₁ s₁ ω)) (fun ω => inner ℝ y (X t₂ s₂ ω)) P = 0) : ProbabilityTheory.iIndepFun (fun t ω s => X t s ω) P - ProbabilityTheory.IsGaussianProcess.indepFun_of_covariance_strongDual 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Independence
{T : Type u_1} {Ω : Type u_2} {E : Type u_3} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] [CompleteSpace E] {S : Type u_4} {X : S → Ω → E} {Y : T → Ω → E} [NormedSpace ℝ E] (hXY : ProbabilityTheory.IsGaussianProcess (Sum.elim X Y) P) (mX : ∀ (s : S), AEMeasurable (X s) P) (mY : ∀ (t : T), AEMeasurable (Y t) P) (h : ∀ (s : S) (t : T) (L₁ L₂ : StrongDual ℝ E), ProbabilityTheory.covariance (⇑L₁ ∘ X s) (⇑L₂ ∘ Y t) P = 0) : ProbabilityTheory.IndepFun (fun ω s => X s ω) (fun ω t => Y t ω) P - ProbabilityTheory.IsGaussianProcess.iIndepFun_of_covariance_strongDual 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Independence
{T : Type u_1} {Ω : Type u_2} {E : Type u_3} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] [CompleteSpace E] {S : T → Type u_4} {X : (t : T) → S t → Ω → E} [NormedSpace ℝ E] (hX : ProbabilityTheory.IsGaussianProcess (fun p ω => X p.fst p.snd ω) P) (mX : ∀ (t : T) (s : S t), AEMeasurable (X t s) P) (h : ∀ (t₁ t₂ : T), t₁ ≠ t₂ → ∀ (s₁ : S t₁) (s₂ : S t₂) (L₁ L₂ : StrongDual ℝ E), ProbabilityTheory.covariance (⇑L₁ ∘ X t₁ s₁) (⇑L₂ ∘ X t₂ s₂) P = 0) : ProbabilityTheory.iIndepFun (fun t ω s => X t s ω) P - ProbabilityTheory.IsPreBrownianReal.covariance_eval 📋 Mathlib.Probability.BrownianMotion.Basic
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {B : NNReal → Ω → ℝ} {P : MeasureTheory.Measure Ω} (hB : ProbabilityTheory.IsPreBrownianReal B P) (s t : NNReal) : ProbabilityTheory.covariance (B s) (B t) P = ↑(min s t) - ProbabilityTheory.IsPreBrownianReal.covariance_fun_eval 📋 Mathlib.Probability.BrownianMotion.Basic
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {B : NNReal → Ω → ℝ} {P : MeasureTheory.Measure Ω} (hB : ProbabilityTheory.IsPreBrownianReal B P) (s t : NNReal) : ProbabilityTheory.covariance (fun ω => B s ω) (fun ω => B t ω) P = ↑(min s t) - ProbabilityTheory.IsGaussianProcess.isPreBrownianReal_of_covariance 📋 Mathlib.Probability.BrownianMotion.Basic
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : NNReal → Ω → ℝ} {P : MeasureTheory.Measure Ω} (h1 : ProbabilityTheory.IsGaussianProcess X P) (h2 : ∀ (t : NNReal), ∫ (x : Ω), X t x ∂P = 0) (h3 : ∀ (s t : NNReal), s ≤ t → ProbabilityTheory.covariance (X s) (X t) P = ↑s) : ProbabilityTheory.IsPreBrownianReal X P
About
Loogle searches Lean and Mathlib definitions and theorems.
You can use Loogle from within the Lean4 VSCode language extension
using the Loogle command from the command palette. You can also try the
#loogle command from LeanSearchClient,
the CLI version, the Loogle
VS Code extension, the lean.nvim
integration or the Zulip bot.
Usage
Loogle finds definitions and lemmas in various ways:
By constant:
🔍Real.sin
finds all lemmas whose statement somehow mentions the sine function.By lemma name substring:
🔍"differ"
finds all lemmas that have"differ"somewhere in their lemma name.By subexpression:
🔍_ * (_ ^ _)
finds all lemmas whose statements somewhere include a product where the second argument is raised to some power.The pattern can also be non-linear, as in
🔍Real.sqrt ?a * Real.sqrt ?aIf the pattern has parameters, they are matched in any order. Both of these will find
List.map:
🔍(?a -> ?b) -> List ?a -> List ?b
🔍List ?a -> (?a -> ?b) -> List ?bBy main conclusion:
🔍|- tsum _ = _ * tsum _
finds all lemmas where the conclusion (the subexpression to the right of all→and∀) has the given shape.As before, if the pattern has parameters, they are matched against the hypotheses of the lemma in any order; for example,
🔍|- _ < _ → tsum _ < tsum _
will findtsum_lt_tsumeven though the hypothesisf i < g iis not the last.You can filter for definitions vs theorems: Using
⊢ (_ : Type _)finds all definitions which provide data while⊢ (_ : Prop)finds all theorems (and definitions of proofs).
If you pass more than one such search filter, separated by commas
Loogle will return lemmas which match all of them. The
search
🔍 Real.sin, "two", tsum, _ * _, _ ^ _, |- _ < _ → _
would find all lemmas which mention the constants Real.sin
and tsum, have "two" as a substring of the
lemma name, include a product and a power somewhere in the type,
and have a hypothesis of the form _ < _ (if
there were any such lemmas). Metavariables (?a) are
assigned independently in each filter.
The #lucky button will directly send you to the
documentation of the first hit.
Source code
You can find the source code for this service at https://github.com/nomeata/loogle. The https://loogle.lean-lang.org/ service is provided by the Lean FRO. Please review the Lean FRO Terms of Use and Privacy Policy.
This is Loogle revision 9f11169 serving mathlib revision ce5dd8c