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Found 69 declarations mentioning ProbabilityTheory.HasGaussianLaw.
- ProbabilityTheory.HasGaussianLaw 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Def
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] [mE : MeasurableSpace E] (X : Ω → E) (P : MeasureTheory.Measure Ω) : Prop - ProbabilityTheory.HasGaussianLaw.aemeasurable 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Def
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] [mE : MeasurableSpace E] {X : Ω → E} {P : MeasureTheory.Measure Ω} (self : ProbabilityTheory.HasGaussianLaw X P) : AEMeasurable X P - ProbabilityTheory.HasGaussianLaw.isGaussian_map 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Def
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] [mE : MeasurableSpace E] {X : Ω → E} {P : MeasureTheory.Measure Ω} (self : ProbabilityTheory.HasGaussianLaw X P) : ProbabilityTheory.IsGaussian (MeasureTheory.Measure.map X P) - ProbabilityTheory.HasGaussianLaw.mk 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Def
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] [mE : MeasurableSpace E] {X : Ω → E} {P : MeasureTheory.Measure Ω} (aemeasurable : AEMeasurable X P := by fun_prop) (isGaussian_map : ProbabilityTheory.IsGaussian (MeasureTheory.Measure.map X P)) : ProbabilityTheory.HasGaussianLaw X P - ProbabilityTheory.IsGaussian.hasGaussianLaw_id 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{E : Type u_2} [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] {mE : MeasurableSpace E} {μ : MeasureTheory.Measure E} [ProbabilityTheory.IsGaussian μ] : ProbabilityTheory.HasGaussianLaw id μ - ProbabilityTheory.HasGaussianLaw.isProbabilityMeasure 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] [mE : MeasurableSpace E] {X : Ω → E} (hX : ProbabilityTheory.HasGaussianLaw X P) : MeasureTheory.IsProbabilityMeasure P - ProbabilityTheory.HasLaw.hasGaussianLaw 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] {mE : MeasurableSpace E} {X : Ω → E} {μ : MeasureTheory.Measure E} (hX : ProbabilityTheory.HasLaw X μ P) [ProbabilityTheory.IsGaussian μ] : ProbabilityTheory.HasGaussianLaw X P - ProbabilityTheory.IsGaussian.hasGaussianLaw 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] [mE : MeasurableSpace E] {X : Ω → E} [ProbabilityTheory.IsGaussian (MeasureTheory.Measure.map X P)] (hX : AEMeasurable X P) : ProbabilityTheory.HasGaussianLaw X P - ProbabilityTheory.HasGaussianLaw.congr 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] [mE : MeasurableSpace E] {X Y : Ω → E} (hX : ProbabilityTheory.HasGaussianLaw X P) (h : X =ᵐ[P] Y) : ProbabilityTheory.HasGaussianLaw Y P - ProbabilityTheory.HasGaussianLaw.map_eq_gaussianReal 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X : Ω → ℝ} (h : ProbabilityTheory.HasGaussianLaw X P) : MeasureTheory.Measure.map X P = ProbabilityTheory.gaussianReal (∫ (x : Ω), X x ∂P) (ProbabilityTheory.variance X P).toNNReal - ProbabilityTheory.HasGaussianLaw.of_subsingleton 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [Subsingleton E] [MeasureTheory.IsProbabilityMeasure P] : ProbabilityTheory.HasGaussianLaw X P - ProbabilityTheory.HasGaussianLaw.integrable 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [CompleteSpace E] [SecondCountableTopology E] (hX : ProbabilityTheory.HasGaussianLaw X P) : MeasureTheory.Integrable X P - ProbabilityTheory.HasGaussianLaw.fun_neg 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] (hX : ProbabilityTheory.HasGaussianLaw X P) : ProbabilityTheory.HasGaussianLaw (fun ω => -X ω) P - ProbabilityTheory.HasGaussianLaw.neg 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] (hX : ProbabilityTheory.HasGaussianLaw X P) : ProbabilityTheory.HasGaussianLaw (-X) P - ProbabilityTheory.HasGaussianLaw.memLp 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [CompleteSpace E] [SecondCountableTopology E] (hX : ProbabilityTheory.HasGaussianLaw X P) {p : ENNReal} (hp : p ≠ ⊤) : MeasureTheory.MemLp X p P - ProbabilityTheory.HasGaussianLaw.memLp_two 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [CompleteSpace E] [SecondCountableTopology E] (hX : ProbabilityTheory.HasGaussianLaw X P) : MeasureTheory.MemLp X 2 P - ProbabilityTheory.HasGaussianLaw.fun_sum 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {ι : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [Fintype ι] {E : Type u_6} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] {X : ι → Ω → E} (hX : ProbabilityTheory.HasGaussianLaw (fun ω x => X x ω) P) : ProbabilityTheory.HasGaussianLaw (fun ω => ∑ i, X i ω) P - ProbabilityTheory.HasGaussianLaw.sum 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {ι : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [Fintype ι] {E : Type u_6} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] {X : ι → Ω → E} (hX : ProbabilityTheory.HasGaussianLaw (fun ω x => X x ω) P) : ProbabilityTheory.HasGaussianLaw (∑ i, X i) P - ProbabilityTheory.HasGaussianLaw.fst 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {F : Type u_3} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [NormedAddCommGroup F] [NormedSpace ℝ F] [MeasurableSpace F] {Y : Ω → F} (hXY : ProbabilityTheory.HasGaussianLaw (fun ω => (X ω, Y ω)) P) : ProbabilityTheory.HasGaussianLaw X P - ProbabilityTheory.HasGaussianLaw.snd 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {F : Type u_3} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] {X : Ω → E} [NormedSpace ℝ E] [NormedAddCommGroup F] [NormedSpace ℝ F] [MeasurableSpace F] [BorelSpace F] {Y : Ω → F} (hXY : ProbabilityTheory.HasGaussianLaw (fun ω => (X ω, Y ω)) P) : ProbabilityTheory.HasGaussianLaw Y P - ProbabilityTheory.HasGaussianLaw.fun_smul 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] (c : ℝ) (hX : ProbabilityTheory.HasGaussianLaw X P) : ProbabilityTheory.HasGaussianLaw (fun ω => c • X ω) P - ProbabilityTheory.HasGaussianLaw.map_of_measurable 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] [mE : MeasurableSpace E] {X : Ω → E} {F : Type u_5} [TopologicalSpace F] [AddCommMonoid F] [Module ℝ F] [MeasurableSpace F] [OpensMeasurableSpace F] (L : E →L[ℝ] F) (hX : ProbabilityTheory.HasGaussianLaw X P) (hL : Measurable ⇑L) : ProbabilityTheory.HasGaussianLaw (⇑L ∘ X) P - ProbabilityTheory.HasGaussianLaw.smul 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] (c : ℝ) (hX : ProbabilityTheory.HasGaussianLaw X P) : ProbabilityTheory.HasGaussianLaw (c • X) P - ProbabilityTheory.HasGaussianLaw.eval 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {ι : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {E : ι → Type u_5} [(i : ι) → NormedAddCommGroup (E i)] [(i : ι) → NormedSpace ℝ (E i)] [(i : ι) → MeasurableSpace (E i)] [∀ (i : ι), BorelSpace (E i)] {X : (i : ι) → Ω → E i} (hX : ProbabilityTheory.HasGaussianLaw (fun ω x => X x ω) P) (i : ι) : ProbabilityTheory.HasGaussianLaw (X i) P - ProbabilityTheory.HasGaussianLaw.fun_sub 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [SecondCountableTopology E] {Y : Ω → E} (hXY : ProbabilityTheory.HasGaussianLaw (fun ω => (X ω, Y ω)) P) : ProbabilityTheory.HasGaussianLaw (fun ω => X ω - Y ω) P - ProbabilityTheory.HasGaussianLaw.fun_add 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [SecondCountableTopology E] {Y : Ω → E} (hXY : ProbabilityTheory.HasGaussianLaw (fun ω => (X ω, Y ω)) P) : ProbabilityTheory.HasGaussianLaw (fun ω => X ω + Y ω) P - ProbabilityTheory.HasGaussianLaw.sub 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [SecondCountableTopology E] {Y : Ω → E} (hXY : ProbabilityTheory.HasGaussianLaw (fun ω => (X ω, Y ω)) P) : ProbabilityTheory.HasGaussianLaw (X - Y) P - ProbabilityTheory.HasGaussianLaw.add 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [SecondCountableTopology E] {Y : Ω → E} (hXY : ProbabilityTheory.HasGaussianLaw (fun ω => (X ω, Y ω)) P) : ProbabilityTheory.HasGaussianLaw (X + Y) P - ProbabilityTheory.HasGaussianLaw.charFun_map_eq 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [InnerProductSpace ℝ E] (t : E) (hX : ProbabilityTheory.HasGaussianLaw X P) : MeasureTheory.charFun (MeasureTheory.Measure.map X P) t = Complex.exp (↑(∫ (x : Ω), (fun ω => inner ℝ t (X ω)) x ∂P) * Complex.I - ↑(ProbabilityTheory.variance (fun ω => inner ℝ t (X ω)) P) / 2) - ProbabilityTheory.hasGaussianLaw_iff_charFun_map_eq 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [CompleteSpace E] [InnerProductSpace ℝ E] [MeasureTheory.IsFiniteMeasure P] (hX : AEMeasurable X P) : ProbabilityTheory.HasGaussianLaw X P ↔ ∀ (t : E), MeasureTheory.charFun (MeasureTheory.Measure.map X P) t = Complex.exp (↑(∫ (x : Ω), (fun ω => inner ℝ t (X ω)) x ∂P) * Complex.I - ↑(ProbabilityTheory.variance (fun ω => inner ℝ t (X ω)) P) / 2) - ProbabilityTheory.HasGaussianLaw.toLp_pi 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {ι : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {E : ι → Type u_5} [(i : ι) → NormedAddCommGroup (E i)] [(i : ι) → NormedSpace ℝ (E i)] [(i : ι) → MeasurableSpace (E i)] [∀ (i : ι), BorelSpace (E i)] {X : (i : ι) → Ω → E i} [∀ (i : ι), SecondCountableTopology (E i)] [Finite ι] (p : ENNReal) [Fact (1 ≤ p)] (hX : ProbabilityTheory.HasGaussianLaw (fun ω x => X x ω) P) : ProbabilityTheory.HasGaussianLaw (fun ω => WithLp.toLp p fun x => X x ω) P - ProbabilityTheory.HasGaussianLaw.prodMk 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {ι : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {E : ι → Type u_5} [(i : ι) → NormedAddCommGroup (E i)] [(i : ι) → NormedSpace ℝ (E i)] [(i : ι) → MeasurableSpace (E i)] [∀ (i : ι), BorelSpace (E i)] {X : (i : ι) → Ω → E i} [∀ (i : ι), SecondCountableTopology (E i)] [Finite ι] (hX : ProbabilityTheory.HasGaussianLaw (fun ω x => X x ω) P) (i j : ι) : ProbabilityTheory.HasGaussianLaw (fun ω => (X i ω, X j ω)) P - ProbabilityTheory.HasGaussianLaw.map_fun 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {F : Type u_3} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [NormedAddCommGroup F] [NormedSpace ℝ F] [MeasurableSpace F] [BorelSpace F] (hX : ProbabilityTheory.HasGaussianLaw X P) (L : E →L[ℝ] F) : ProbabilityTheory.HasGaussianLaw (fun ω => L (X ω)) P - ProbabilityTheory.HasGaussianLaw.map 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {F : Type u_3} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [NormedAddCommGroup F] [NormedSpace ℝ F] [MeasurableSpace F] [BorelSpace F] (hX : ProbabilityTheory.HasGaussianLaw X P) (L : E →L[ℝ] F) : ProbabilityTheory.HasGaussianLaw (⇑L ∘ X) P - ProbabilityTheory.HasGaussianLaw.toLp_prodMk 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {F : Type u_3} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [NormedAddCommGroup F] [NormedSpace ℝ F] [MeasurableSpace F] [BorelSpace F] {Y : Ω → F} [SecondCountableTopologyEither E F] (p : ENNReal) [Fact (1 ≤ p)] (hXY : ProbabilityTheory.HasGaussianLaw (fun ω => (X ω, Y ω)) P) : ProbabilityTheory.HasGaussianLaw (fun ω => WithLp.toLp p (X ω, Y ω)) P - ProbabilityTheory.HasGaussianLaw.charFunDual_map_eq_fun 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] (L : StrongDual ℝ E) (hX : ProbabilityTheory.HasGaussianLaw X P) : MeasureTheory.charFunDual (MeasureTheory.Measure.map X P) L = Complex.exp (↑(∫ (ω : Ω), L (X ω) ∂P) * Complex.I - ↑(ProbabilityTheory.variance (fun ω => L (X ω)) P) / 2) - ProbabilityTheory.HasGaussianLaw.charFunDual_map_eq 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] (L : StrongDual ℝ E) (hX : ProbabilityTheory.HasGaussianLaw X P) : MeasureTheory.charFunDual (MeasureTheory.Measure.map X P) L = Complex.exp (↑(∫ (x : Ω), (⇑L ∘ X) x ∂P) * Complex.I - ↑(ProbabilityTheory.variance (⇑L ∘ X) P) / 2) - ProbabilityTheory.hasGaussianLaw_iff_charFunDual_map_eq 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [MeasureTheory.IsFiniteMeasure P] (hX : AEMeasurable X P) : ProbabilityTheory.HasGaussianLaw X P ↔ ∀ (L : StrongDual ℝ E), MeasureTheory.charFunDual (MeasureTheory.Measure.map X P) L = Complex.exp (↑(∫ (x : Ω), (⇑L ∘ X) x ∂P) * Complex.I - ↑(ProbabilityTheory.variance (⇑L ∘ X) P) / 2) - ProbabilityTheory.HasGaussianLaw.map_equiv_fun 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {F : Type u_3} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [NormedAddCommGroup F] [NormedSpace ℝ F] [MeasurableSpace F] [BorelSpace F] (hX : ProbabilityTheory.HasGaussianLaw X P) (L : E ≃L[ℝ] F) : ProbabilityTheory.HasGaussianLaw (fun ω => L (X ω)) P - ProbabilityTheory.HasGaussianLaw.map_equiv 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Basic
{Ω : Type u_1} {E : Type u_2} {F : Type u_3} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] {X : Ω → E} [NormedSpace ℝ E] [NormedAddCommGroup F] [NormedSpace ℝ F] [MeasurableSpace F] [BorelSpace F] (hX : ProbabilityTheory.HasGaussianLaw X P) (L : E ≃L[ℝ] F) : ProbabilityTheory.HasGaussianLaw (⇑L ∘ X) P - ProbabilityTheory.IsGaussianProcess.hasGaussianLaw 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Def
{Ω : Type u_1} {E : Type u_2} {T : Type u_3} {mΩ : MeasurableSpace Ω} [MeasurableSpace E] [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] {X : T → Ω → E} {P : autoParam (MeasureTheory.Measure Ω) ProbabilityTheory.IsGaussianProcess._auto_1} (self : ProbabilityTheory.IsGaussianProcess X P) (I : Finset T) : ProbabilityTheory.HasGaussianLaw (fun ω => I.restrict fun x => X x ω) P - ProbabilityTheory.IsGaussianProcess.mk 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Def
{Ω : Type u_1} {E : Type u_2} {T : Type u_3} {mΩ : MeasurableSpace Ω} [MeasurableSpace E] [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] {X : T → Ω → E} {P : autoParam (MeasureTheory.Measure Ω) ProbabilityTheory.IsGaussianProcess._auto_1} (hasGaussianLaw : ∀ (I : Finset T), ProbabilityTheory.HasGaussianLaw (fun ω => I.restrict fun x => X x ω) P) : ProbabilityTheory.IsGaussianProcess X P - 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.iIndepFun.hasGaussianLaw_fun_sum 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] [CompleteSpace E] {ι : Type u_3} [Fintype ι] {X : ι → Ω → E} (hX1 : ∀ (i : ι), ProbabilityTheory.HasGaussianLaw (X i) P) (hX2 : ProbabilityTheory.iIndepFun X P) : ProbabilityTheory.HasGaussianLaw (fun ω => ∑ i, X i ω) P - ProbabilityTheory.iIndepFun.hasGaussianLaw_sum 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] [CompleteSpace E] {ι : Type u_3} [Fintype ι] {X : ι → Ω → E} (hX1 : ∀ (i : ι), ProbabilityTheory.HasGaussianLaw (X i) P) (hX2 : ProbabilityTheory.iIndepFun X P) : ProbabilityTheory.HasGaussianLaw (∑ i, X i) P - ProbabilityTheory.iIndepFun.hasGaussianLaw_fun_sub 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] [CompleteSpace E] {X Y : Ω → E} (hX : ProbabilityTheory.HasGaussianLaw X P) (hY : ProbabilityTheory.HasGaussianLaw Y P) (h : ProbabilityTheory.IndepFun X Y P) : ProbabilityTheory.HasGaussianLaw (fun ω => X ω - Y ω) 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.iIndepFun.hasGaussianLaw_fun_add 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] [CompleteSpace E] {X Y : Ω → E} (hX : ProbabilityTheory.HasGaussianLaw X P) (hY : ProbabilityTheory.HasGaussianLaw Y P) (h : ProbabilityTheory.IndepFun X Y P) : ProbabilityTheory.HasGaussianLaw (fun ω => X ω + Y ω) P - ProbabilityTheory.iIndepFun.hasGaussianLaw_sub 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] [CompleteSpace E] {X Y : Ω → E} (hX : ProbabilityTheory.HasGaussianLaw X P) (hY : ProbabilityTheory.HasGaussianLaw Y P) (h : ProbabilityTheory.IndepFun X Y P) : ProbabilityTheory.HasGaussianLaw (X - Y) P - ProbabilityTheory.iIndepFun.hasGaussianLaw_add 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] [CompleteSpace E] {X Y : Ω → E} (hX : ProbabilityTheory.HasGaussianLaw X P) (hY : ProbabilityTheory.HasGaussianLaw Y P) (h : ProbabilityTheory.IndepFun X Y P) : ProbabilityTheory.HasGaussianLaw (X + Y) 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.IndepFun.hasGaussianLaw_sub_of_sub 📋 Mathlib.Probability.Distributions.Gaussian.HasGaussianLaw.Independence
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {E : Type u_2} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] {X Y : Ω → E} (hX : ProbabilityTheory.HasGaussianLaw X P) (hY : ProbabilityTheory.HasGaussianLaw Y P) (h : ProbabilityTheory.IndepFun X (Y - X) P) : ProbabilityTheory.HasGaussianLaw (Y - X) P - ProbabilityTheory.iIndepFun.hasGaussianLaw 📋 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} (hX1 : ∀ (i : ι), ProbabilityTheory.HasGaussianLaw (X i) P) (hX2 : ProbabilityTheory.iIndepFun X P) : ProbabilityTheory.HasGaussianLaw (fun ω x => X x 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.IndepFun.hasGaussianLaw 📋 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} (hX : ProbabilityTheory.HasGaussianLaw X P) (hY : ProbabilityTheory.HasGaussianLaw Y P) (hXY : ProbabilityTheory.IndepFun X Y P) : ProbabilityTheory.HasGaussianLaw (fun ω => (X ω, Y ω)) 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.hasGaussianLaw_eval 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Basic
{T : Type u_2} {Ω : Type u_3} {E : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X : T → Ω → E} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [NormedSpace ℝ E] (hX : ProbabilityTheory.IsGaussianProcess X P) (t : T) : ProbabilityTheory.HasGaussianLaw (X t) P - ProbabilityTheory.IsGaussianProcess.hasGaussianLaw_fun_sum 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Basic
{T : Type u_2} {Ω : Type u_3} {E : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X : T → Ω → E} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [NormedSpace ℝ E] [SecondCountableTopology E] (hX : ProbabilityTheory.IsGaussianProcess X P) {I : Finset T} : ProbabilityTheory.HasGaussianLaw (fun ω => ∑ i ∈ I, X i ω) P - ProbabilityTheory.IsGaussianProcess.hasGaussianLaw_sum 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Basic
{T : Type u_2} {Ω : Type u_3} {E : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X : T → Ω → E} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [NormedSpace ℝ E] [SecondCountableTopology E] (hX : ProbabilityTheory.IsGaussianProcess X P) {I : Finset T} : ProbabilityTheory.HasGaussianLaw (∑ i ∈ I, X i) P - ProbabilityTheory.IsGaussianProcess.hasGaussianLaw_fun_sub 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Basic
{T : Type u_2} {Ω : Type u_3} {E : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X : T → Ω → E} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [NormedSpace ℝ E] [SecondCountableTopology E] (hX : ProbabilityTheory.IsGaussianProcess X P) {s t : T} : ProbabilityTheory.HasGaussianLaw (fun ω => X s ω - X t ω) P - ProbabilityTheory.IsGaussianProcess.hasGaussianLaw_fun_add 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Basic
{T : Type u_2} {Ω : Type u_3} {E : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X : T → Ω → E} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [NormedSpace ℝ E] [SecondCountableTopology E] (hX : ProbabilityTheory.IsGaussianProcess X P) {s t : T} : ProbabilityTheory.HasGaussianLaw (fun ω => X s ω + X t ω) P - ProbabilityTheory.IsGaussianProcess.hasGaussianLaw_sub 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Basic
{T : Type u_2} {Ω : Type u_3} {E : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X : T → Ω → E} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [NormedSpace ℝ E] [SecondCountableTopology E] (hX : ProbabilityTheory.IsGaussianProcess X P) {s t : T} : ProbabilityTheory.HasGaussianLaw (X s - X t) P - ProbabilityTheory.IsGaussianProcess.hasGaussianLaw_add 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Basic
{T : Type u_2} {Ω : Type u_3} {E : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X : T → Ω → E} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [NormedSpace ℝ E] [SecondCountableTopology E] (hX : ProbabilityTheory.IsGaussianProcess X P) {s t : T} : ProbabilityTheory.HasGaussianLaw (X s + X t) P - ProbabilityTheory.IsGaussianProcess.hasGaussianLaw_prodMk 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Basic
{T : Type u_2} {Ω : Type u_3} {E : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X : T → Ω → E} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [NormedSpace ℝ E] [SecondCountableTopology E] (hX : ProbabilityTheory.IsGaussianProcess X P) {s t : T} : ProbabilityTheory.HasGaussianLaw (fun ω => (X s ω, X t ω)) P - ProbabilityTheory.IsGaussianProcess.hasGaussianLaw_increments 📋 Mathlib.Probability.Distributions.Gaussian.IsGaussianProcess.Basic
{T : Type u_2} {Ω : Type u_3} {E : Type u_4} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {X : T → Ω → E} [NormedAddCommGroup E] [MeasurableSpace E] [BorelSpace E] [NormedSpace ℝ E] [SecondCountableTopology E] (hX : ProbabilityTheory.IsGaussianProcess X P) {n : ℕ} {t : Fin (n + 1) → T} : ProbabilityTheory.HasGaussianLaw (fun ω i => X (t i.succ) ω - X (t i.castSucc) ω) P - ProbabilityTheory.HasIndepIncrements.isGaussianProcess 📋 Mathlib.Probability.Independence.Process.HasIndepIncrements.IsGaussianProcess
{T : Type u_1} {Ω : Type u_2} {E : Type u_3} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} [LinearOrder T] [OrderBot T] [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] [SecondCountableTopology E] [CompleteSpace E] {X : T → Ω → E} (law : ∀ (t : T), ProbabilityTheory.HasGaussianLaw (X t) P) (h_bot : ∀ᵐ (ω : Ω) ∂P, X ⊥ ω = 0) (incr : ProbabilityTheory.HasIndepIncrements X P) : ProbabilityTheory.IsGaussianProcess 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