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Found 87 declarations mentioning ProbabilityTheory.variance.
- ProbabilityTheory.variance 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} (X : Ω → ℝ) (μ : MeasureTheory.Measure Ω) : ℝ - ProbabilityTheory.variance_nonneg 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} (X : Ω → ℝ) (μ : MeasureTheory.Measure Ω) : 0 ≤ ProbabilityTheory.variance X μ - ProbabilityTheory.variance_dirac 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} [MeasurableSingletonClass Ω] (x : Ω) : ProbabilityTheory.variance X (MeasureTheory.Measure.dirac x) = 0 - ProbabilityTheory.variance_fun_neg 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} : ProbabilityTheory.variance (fun ω => -X ω) μ = ProbabilityTheory.variance X 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_neg 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} : ProbabilityTheory.variance (-X) μ = ProbabilityTheory.variance X μ - ProbabilityTheory.variance_zero_measure 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} : ProbabilityTheory.variance X 0 = 0 - ProbabilityTheory.variance_zero 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} (μ : MeasureTheory.Measure Ω) : ProbabilityTheory.variance 0 μ = 0 - ProbabilityTheory.variance_id_map 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X : Ω → ℝ} (hX : AEMeasurable X μ) : ProbabilityTheory.variance id (MeasureTheory.Measure.map X μ) = ProbabilityTheory.variance X μ - ProbabilityTheory.variance_congr 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X Y : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (h : X =ᵐ[μ] Y) : ProbabilityTheory.variance X μ = ProbabilityTheory.variance Y μ - MeasureTheory.MeasurePreserving.variance_fun_comp 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {Ω' : Type u_3} {mΩ' : MeasurableSpace Ω'} {ν : MeasureTheory.Measure Ω'} {X : Ω → Ω'} (hX : MeasureTheory.MeasurePreserving X μ ν) {f : Ω' → ℝ} (hf : AEMeasurable f ν) : ProbabilityTheory.variance (fun ω => f (X ω)) μ = ProbabilityTheory.variance f ν - ProbabilityTheory.variance_add_const 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hX : MeasureTheory.AEStronglyMeasurable X μ) (c : ℝ) : ProbabilityTheory.variance (fun ω => X ω + c) μ = ProbabilityTheory.variance X μ - ProbabilityTheory.variance_const_add 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hX : MeasureTheory.AEStronglyMeasurable X μ) (c : ℝ) : ProbabilityTheory.variance (fun ω => c + X ω) μ = ProbabilityTheory.variance X μ - ProbabilityTheory.variance_const_sub 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hX : MeasureTheory.AEStronglyMeasurable X μ) (c : ℝ) : ProbabilityTheory.variance (fun ω => c - X ω) μ = ProbabilityTheory.variance X μ - ProbabilityTheory.variance_sub_const 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hX : MeasureTheory.AEStronglyMeasurable X μ) (c : ℝ) : ProbabilityTheory.variance (fun ω => X ω - c) μ = ProbabilityTheory.variance X μ - ProbabilityTheory.variance_const_mul 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} (c : ℝ) (X : Ω → ℝ) (μ : MeasureTheory.Measure Ω) : ProbabilityTheory.variance (fun ω => c * X ω) μ = c ^ 2 * ProbabilityTheory.variance X μ - ProbabilityTheory.variance_map 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {Ω' : Type u_3} {mΩ' : MeasurableSpace Ω'} {μ : MeasureTheory.Measure Ω'} {Y : Ω' → Ω} (hX : AEMeasurable X (MeasureTheory.Measure.map Y μ)) (hY : AEMeasurable Y μ) : ProbabilityTheory.variance X (MeasureTheory.Measure.map Y μ) = ProbabilityTheory.variance (X ∘ Y) μ - ProbabilityTheory.variance_mul_const 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} (c : ℝ) (X : Ω → ℝ) (μ : MeasureTheory.Measure Ω) : ProbabilityTheory.variance (fun ω => X ω * c) μ = ProbabilityTheory.variance X μ * c ^ 2 - ProbabilityTheory.variance_smul 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} (c : ℝ) (X : Ω → ℝ) (μ : MeasureTheory.Measure Ω) : ProbabilityTheory.variance (c • X) μ = c ^ 2 * ProbabilityTheory.variance X μ - ProbabilityTheory.variance_le_expectation_sq 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] {X : Ω → ℝ} (hm : MeasureTheory.AEStronglyMeasurable X μ) : ProbabilityTheory.variance X μ ≤ ∫ (x : Ω), (X ^ 2) x ∂μ - ProbabilityTheory.variance_map_equiv 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {Ω' : Type u_3} {mΩ' : MeasurableSpace Ω'} {μ : MeasureTheory.Measure Ω'} (X : Ω → ℝ) (Y : Ω' ≃ᵐ Ω) : ProbabilityTheory.variance X (MeasureTheory.Measure.map (⇑Y) μ) = ProbabilityTheory.variance (X ∘ ⇑Y) μ - ProbabilityTheory.variance_eq_integral 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (hX : AEMeasurable X μ) : ProbabilityTheory.variance X μ = ∫ (ω : Ω), (X ω - ∫ (x : Ω), X x ∂μ) ^ 2 ∂μ - ProbabilityTheory.variance_of_integral_eq_zero 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (hX : AEMeasurable X μ) (hXint : ∫ (x : Ω), X x ∂μ = 0) : ProbabilityTheory.variance X μ = ∫ (ω : Ω), X ω ^ 2 ∂μ - ProbabilityTheory.variance_smul' 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {A : Type u_2} [CommSemiring A] [Algebra A ℝ] (c : A) (X : Ω → ℝ) (μ : MeasureTheory.Measure Ω) : ProbabilityTheory.variance (c • X) μ = c ^ 2 • ProbabilityTheory.variance X μ - ProbabilityTheory.ofReal_variance 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) : ENNReal.ofReal (ProbabilityTheory.variance X μ) = ProbabilityTheory.evariance X μ - MeasureTheory.MemLp.ofReal_variance_eq 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) : ENNReal.ofReal (ProbabilityTheory.variance X μ) = ProbabilityTheory.evariance X μ - ProbabilityTheory.memLp_two_of_variance_ne_zero 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.AEStronglyMeasurable X μ) (h : ProbabilityTheory.variance X μ ≠ 0) : MeasureTheory.MemLp X 2 μ - ProbabilityTheory.variance_of_not_memLp 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.AEStronglyMeasurable X μ) (hX_not : ¬MeasureTheory.MemLp X 2 μ) : ProbabilityTheory.variance X μ = 0 - ProbabilityTheory.variance_le_sq_of_bounded 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] {a b : ℝ} {X : Ω → ℝ} (h : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b) (hX : AEMeasurable X μ) : ProbabilityTheory.variance X μ ≤ ((b - a) / 2) ^ 2 - ProbabilityTheory.variance_le_sub_mul_sub 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] {a b : ℝ} {X : Ω → ℝ} (h : ∀ᵐ (ω : Ω) ∂μ, X ω ∈ Set.Icc a b) (hX : AEMeasurable X μ) : ProbabilityTheory.variance X μ ≤ (b - ∫ (x : Ω), X x ∂μ) * (∫ (x : Ω), X x ∂μ - a) - 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.ae_eq_integral_of_variance_eq_zero 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) (h : ProbabilityTheory.variance X μ = 0) : ∀ᵐ (ω : Ω) ∂μ, X ω = ∫ (x : Ω), X x ∂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.IndepFun.variance_sum 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {ι : Type u_3} {X : ι → Ω → ℝ} {s : Finset ι} (hs : ∀ i ∈ s, MeasureTheory.MemLp (X i) 2 μ) (h : (↑s).Pairwise fun i j => ProbabilityTheory.IndepFun (X i) (X j) μ) : ProbabilityTheory.variance (∑ i ∈ s, X i) μ = ∑ i ∈ s, ProbabilityTheory.variance (X i) μ - ProbabilityTheory.variance_sum_pi 📋 Mathlib.Probability.Moments.Variance
{ι : Type u_2} [Fintype ι] {Ω : ι → Type u_3} {mΩ : (i : ι) → MeasurableSpace (Ω i)} {μ : (i : ι) → MeasureTheory.Measure (Ω i)} [∀ (i : ι), MeasureTheory.IsProbabilityMeasure (μ i)] {X : (i : ι) → Ω i → ℝ} (h : ∀ (i : ι), MeasureTheory.MemLp (X i) 2 (μ i)) : ProbabilityTheory.variance (∑ i, fun ω => X i (ω i)) (MeasureTheory.Measure.pi μ) = ∑ i, ProbabilityTheory.variance (X i) (μ i) - ProbabilityTheory.variance_eq_sub 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] {X : Ω → ℝ} (hX : MeasureTheory.MemLp X 2 μ) : ProbabilityTheory.variance X μ = ∫ (x : Ω), (X ^ 2) x ∂μ - (∫ (x : Ω), X x ∂μ) ^ 2 - ProbabilityTheory.meas_ge_le_variance_div_sq 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsFiniteMeasure μ] {X : Ω → ℝ} (hX : MeasureTheory.MemLp X 2 μ) {c : ℝ} (hc : 0 < c) : μ {ω | c ≤ |X ω - ∫ (x : Ω), X x ∂μ|} ≤ ENNReal.ofReal (ProbabilityTheory.variance X μ / c ^ 2) - ProbabilityTheory.IndepFun.variance_fun_add 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X Y : Ω → ℝ} (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (h : ProbabilityTheory.IndepFun X Y μ) : ProbabilityTheory.variance (fun ω => X ω + Y ω) μ = ProbabilityTheory.variance X μ + ProbabilityTheory.variance Y μ - ProbabilityTheory.IndepFun.variance_add 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X Y : Ω → ℝ} (hX : MeasureTheory.MemLp X 2 μ) (hY : MeasureTheory.MemLp Y 2 μ) (h : ProbabilityTheory.IndepFun X Y μ) : ProbabilityTheory.variance (X + Y) μ = ProbabilityTheory.variance X μ + ProbabilityTheory.variance Y μ - ProbabilityTheory.variance_add_prod 📋 Mathlib.Probability.Moments.Variance
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {Ω' : Type u_3} {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.variance (fun p => X p.1 + Y p.2) (μ.prod ν) = ProbabilityTheory.variance X μ + ProbabilityTheory.variance Y ν - 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.variance_dual_prod 📋 Mathlib.Probability.Moments.Variance
{E : Type u_3} {F : Type u_4} [NormedAddCommGroup E] [NormedSpace ℝ E] {mE : MeasurableSpace E} [NormedAddCommGroup F] [NormedSpace ℝ F] {mF : MeasurableSpace F} {μ : MeasureTheory.Measure E} [MeasureTheory.IsProbabilityMeasure μ] {ν : MeasureTheory.Measure F} [MeasureTheory.IsProbabilityMeasure ν] {L : StrongDual ℝ (E × F)} (hLμ : MeasureTheory.MemLp id 2 μ) (hLν : MeasureTheory.MemLp id 2 ν) : ProbabilityTheory.variance (⇑L) (μ.prod ν) = ProbabilityTheory.variance (⇑(L ∘SL ContinuousLinearMap.inl ℝ E F)) μ + ProbabilityTheory.variance (⇑(L ∘SL ContinuousLinearMap.inr ℝ E F)) ν - ProbabilityTheory.variance_dual_prod' 📋 Mathlib.Probability.Moments.Variance
{E : Type u_3} {F : Type u_4} [NormedAddCommGroup E] [NormedSpace ℝ E] {mE : MeasurableSpace E} [NormedAddCommGroup F] [NormedSpace ℝ F] {mF : MeasurableSpace F} {μ : MeasureTheory.Measure E} [MeasureTheory.IsProbabilityMeasure μ] {ν : MeasureTheory.Measure F} [MeasureTheory.IsProbabilityMeasure ν] {L : StrongDual ℝ (E × F)} (hLμ : MeasureTheory.MemLp (⇑(L ∘SL ContinuousLinearMap.inl ℝ E F)) 2 μ) (hLν : MeasureTheory.MemLp (⇑(L ∘SL ContinuousLinearMap.inr ℝ E F)) 2 ν) : ProbabilityTheory.variance (⇑L) (μ.prod ν) = ProbabilityTheory.variance (⇑(L ∘SL ContinuousLinearMap.inl ℝ E F)) μ + ProbabilityTheory.variance (⇑(L ∘SL ContinuousLinearMap.inr ℝ E F)) ν - ProbabilityTheory.HasLaw.variance_eq 📋 Mathlib.Probability.HasLaw
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {P : MeasureTheory.Measure Ω} {μ : MeasureTheory.Measure ℝ} {X : Ω → ℝ} (hX : ProbabilityTheory.HasLaw X μ P) : ProbabilityTheory.variance X P = ProbabilityTheory.variance id μ - ProbabilityTheory.IdentDistrib.variance_eq 📋 Mathlib.Probability.IdentDistrib
{α : Type u_1} {β : Type u_2} [MeasurableSpace α] [MeasurableSpace β] {μ : MeasureTheory.Measure α} {ν : MeasureTheory.Measure β} {f : α → ℝ} {g : β → ℝ} (h : ProbabilityTheory.IdentDistrib f g μ ν) : ProbabilityTheory.variance f μ = ProbabilityTheory.variance g ν - ProbabilityTheory.centralMoment_two_eq_variance 📋 Mathlib.Probability.Moments.Basic
{Ω : Type u_1} {m : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (hX : AEMeasurable X μ) : ProbabilityTheory.centralMoment X 2 μ = ProbabilityTheory.variance X μ - ProbabilityTheory.variance_fun_id_gaussianReal 📋 Mathlib.Probability.Distributions.Gaussian.Real
{μ : ℝ} {v : NNReal} : ProbabilityTheory.variance (fun x => x) (ProbabilityTheory.gaussianReal μ v) = ↑v - ProbabilityTheory.variance_id_gaussianReal 📋 Mathlib.Probability.Distributions.Gaussian.Real
{μ : ℝ} {v : NNReal} : ProbabilityTheory.variance id (ProbabilityTheory.gaussianReal μ v) = ↑v - ProbabilityTheory.variance_linearMap_gaussianReal 📋 Mathlib.Probability.Distributions.Gaussian.Real
{μ : ℝ} {v : NNReal} (L : ℝ →ₗ[ℝ] ℝ) : ProbabilityTheory.variance (⇑L) (ProbabilityTheory.gaussianReal μ v) = ↑(L 1 ^ 2).toNNReal * ↑v - ProbabilityTheory.variance_continuousLinearMap_gaussianReal 📋 Mathlib.Probability.Distributions.Gaussian.Real
{μ : ℝ} {v : NNReal} (L : ℝ →L[ℝ] ℝ) : ProbabilityTheory.variance (⇑L) (ProbabilityTheory.gaussianReal μ v) = ↑(L 1 ^ 2).toNNReal * ↑v - ProbabilityTheory.IsGaussian.eq_gaussianReal 📋 Mathlib.Probability.Distributions.Gaussian.Basic
(μ : MeasureTheory.Measure ℝ) (h : ProbabilityTheory.IsGaussian μ) : μ = ProbabilityTheory.gaussianReal (∫ (x : ℝ), id x ∂μ) (ProbabilityTheory.variance id μ).toNNReal - ProbabilityTheory.IsGaussian.charFun_eq 📋 Mathlib.Probability.Distributions.Gaussian.Basic
{E : Type u_3} [NormedAddCommGroup E] [InnerProductSpace ℝ E] [MeasurableSpace E] [BorelSpace E] {μ : MeasureTheory.Measure E} [ProbabilityTheory.IsGaussian μ] (t : E) : MeasureTheory.charFun μ t = Complex.exp ((∫ (x : E), ↑((fun x => inner ℝ t x) x) ∂μ) * Complex.I - ↑(ProbabilityTheory.variance (fun x => inner ℝ t x) μ) / 2) - ProbabilityTheory.isGaussian_iff_charFun_eq 📋 Mathlib.Probability.Distributions.Gaussian.Basic
{E : Type u_3} [NormedAddCommGroup E] [InnerProductSpace ℝ E] [MeasurableSpace E] [BorelSpace E] {μ : MeasureTheory.Measure E} [CompleteSpace E] [MeasureTheory.IsFiniteMeasure μ] : ProbabilityTheory.IsGaussian μ ↔ ∀ (t : E), MeasureTheory.charFun μ t = Complex.exp ((∫ (x : E), ↑((fun x => inner ℝ t x) x) ∂μ) * Complex.I - ↑(ProbabilityTheory.variance (fun x => inner ℝ t x) μ) / 2) - ProbabilityTheory.IsGaussian.map_eq_gaussianReal 📋 Mathlib.Probability.Distributions.Gaussian.Basic
{E : Type u_1} {inst✝ : TopologicalSpace E} {inst✝¹ : AddCommMonoid E} {inst✝² : Module ℝ E} {mE : MeasurableSpace E} {μ : MeasureTheory.Measure E} [self : ProbabilityTheory.IsGaussian μ] (L : StrongDual ℝ E) : MeasureTheory.Measure.map (⇑L) μ = ProbabilityTheory.gaussianReal (∫ (x : E), L x ∂μ) (ProbabilityTheory.variance (⇑L) μ).toNNReal - ProbabilityTheory.IsGaussian.mk 📋 Mathlib.Probability.Distributions.Gaussian.Basic
{E : Type u_1} [TopologicalSpace E] [AddCommMonoid E] [Module ℝ E] {mE : MeasurableSpace E} {μ : MeasureTheory.Measure E} (map_eq_gaussianReal : ∀ (L : StrongDual ℝ E), MeasureTheory.Measure.map (⇑L) μ = ProbabilityTheory.gaussianReal (∫ (x : E), L x ∂μ) (ProbabilityTheory.variance (⇑L) μ).toNNReal) : ProbabilityTheory.IsGaussian μ - ProbabilityTheory.IsGaussian.charFunDual_eq 📋 Mathlib.Probability.Distributions.Gaussian.Basic
{E : Type u_1} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] {μ : MeasureTheory.Measure E} [ProbabilityTheory.IsGaussian μ] (L : StrongDual ℝ E) : MeasureTheory.charFunDual μ L = Complex.exp ((∫ (x : E), ↑(L x) ∂μ) * Complex.I - ↑(ProbabilityTheory.variance (⇑L) μ) / 2) - ProbabilityTheory.isGaussian_of_charFunDual_eq 📋 Mathlib.Probability.Distributions.Gaussian.Basic
{E : Type u_1} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] {μ : MeasureTheory.Measure E} [MeasureTheory.IsFiniteMeasure μ] : (∀ (L : StrongDual ℝ E), MeasureTheory.charFunDual μ L = Complex.exp ((∫ (x : E), ↑(L x) ∂μ) * Complex.I - ↑(ProbabilityTheory.variance (⇑L) μ) / 2)) → ProbabilityTheory.IsGaussian μ - ProbabilityTheory.isGaussian_iff_charFunDual_eq 📋 Mathlib.Probability.Distributions.Gaussian.Basic
{E : Type u_1} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] {μ : MeasureTheory.Measure E} [MeasureTheory.IsFiniteMeasure μ] : ProbabilityTheory.IsGaussian μ ↔ ∀ (L : StrongDual ℝ E), MeasureTheory.charFunDual μ L = Complex.exp ((∫ (x : E), ↑(L x) ∂μ) * Complex.I - ↑(ProbabilityTheory.variance (⇑L) μ) / 2) - ProbabilityTheory.covarianceBilinDual_self_eq_variance 📋 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) (L : StrongDual ℝ E) : ((ProbabilityTheory.covarianceBilinDual μ) L) L = ProbabilityTheory.variance (⇑L) μ - ProbabilityTheory.covarianceBilin_real 📋 Mathlib.Probability.Moments.CovarianceBilin
{μ : MeasureTheory.Measure ℝ} [MeasureTheory.IsFiniteMeasure μ] (x y : ℝ) : ((ProbabilityTheory.covarianceBilin μ) x) y = x * y * ProbabilityTheory.variance id μ - ProbabilityTheory.covarianceBilin_real_self 📋 Mathlib.Probability.Moments.CovarianceBilin
{μ : MeasureTheory.Measure ℝ} [MeasureTheory.IsFiniteMeasure μ] (x : ℝ) : ((ProbabilityTheory.covarianceBilin μ) x) x = x ^ 2 * ProbabilityTheory.variance id μ - ProbabilityTheory.covarianceBilin_self 📋 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 : E) : ((ProbabilityTheory.covarianceBilin μ) x) x = ProbabilityTheory.variance (fun u => inner ℝ x u) μ - ProbabilityTheory.covarianceBilin_apply_basisFun_self 📋 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 : 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) i) = ProbabilityTheory.variance (X i) μ - ProbabilityTheory.IsGaussian.eq_dirac_of_variance_eq_zero 📋 Mathlib.Probability.Distributions.Gaussian.Fernique
{E : Type u_1} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] {μ : MeasureTheory.Measure E} [ProbabilityTheory.IsGaussian μ] [CompleteSpace E] [SecondCountableTopology E] (h : ∀ (L : StrongDual ℝ E), ProbabilityTheory.variance (⇑L) μ = 0) : μ = MeasureTheory.Measure.dirac (∫ (x : E), x ∂μ) - ProbabilityTheory.IsGaussian.charFunDual_eq_of_integral_eq_zero 📋 Mathlib.Probability.Distributions.Gaussian.Fernique
{E : Type u_1} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] {μ : MeasureTheory.Measure E} [ProbabilityTheory.IsGaussian μ] [CompleteSpace E] [SecondCountableTopology E] (hμ : ∫ (x : E), id x ∂μ = 0) (L : StrongDual ℝ E) : MeasureTheory.charFunDual μ L = Complex.exp (-↑(ProbabilityTheory.variance (⇑L) μ) / 2) - ProbabilityTheory.IsGaussian.charFunDual_eq_of_forall_strongDual_eq_zero 📋 Mathlib.Probability.Distributions.Gaussian.Fernique
{E : Type u_1} [NormedAddCommGroup E] [NormedSpace ℝ E] [MeasurableSpace E] [BorelSpace E] {μ : MeasureTheory.Measure E} [ProbabilityTheory.IsGaussian μ] (hμ : ∀ (L : StrongDual ℝ E), ∫ (x : E), L x ∂μ = 0) (L : StrongDual ℝ E) : MeasureTheory.charFunDual μ L = Complex.exp (-↑(ProbabilityTheory.variance (⇑L) μ) / 2) - ProbabilityTheory.variance_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 : ι) : ProbabilityTheory.variance (fun x => x.ofLp i) (ProbabilityTheory.multivariateGaussian μ S) = S i i - ProbabilityTheory.variance_dual_stdGaussian 📋 Mathlib.Probability.Distributions.Gaussian.Multivariate
{E : Type u_2} [NormedAddCommGroup E] [InnerProductSpace ℝ E] [FiniteDimensional ℝ E] [MeasurableSpace E] [BorelSpace E] (L : StrongDual ℝ E) : ProbabilityTheory.variance (⇑L) (ProbabilityTheory.stdGaussian E) = ‖L‖ ^ 2 - 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.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.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.BrownianReal.variance_eval_projectiveFamily 📋 Mathlib.Probability.BrownianMotion.GaussianProjectiveFamily
{I : Finset NNReal} (s : ↥I) : ProbabilityTheory.variance (fun x => x s) (ProbabilityTheory.BrownianReal.projectiveFamily I) = ↑↑s - ProbabilityTheory.BrownianReal.variance_fun_projectiveFamily 📋 Mathlib.Probability.BrownianMotion.GaussianProjectiveFamily
(I : Finset NNReal) (f : (↥I → ℝ) → ℝ) : ProbabilityTheory.variance f (ProbabilityTheory.BrownianReal.projectiveFamily I) = ProbabilityTheory.variance (fun x => f x.ofLp) (ProbabilityTheory.multivariateGaussian 0 (ProbabilityTheory.BrownianReal.covMatrix I)) - ProbabilityTheory.BrownianReal.variance_projectiveFamily 📋 Mathlib.Probability.BrownianMotion.GaussianProjectiveFamily
(I : Finset NNReal) (f : (↥I → ℝ) → ℝ) : ProbabilityTheory.variance f (ProbabilityTheory.BrownianReal.projectiveFamily I) = ProbabilityTheory.variance (f ∘ WithLp.ofLp) (ProbabilityTheory.multivariateGaussian 0 (ProbabilityTheory.BrownianReal.covMatrix I)) - ProbabilityTheory.tendstoInDistribution_inv_sqrt_mul_sum_sub 📋 Mathlib.Probability.CentralLimitTheorem
{Ω : Type u_1} {Ω' : Type u_2} {mΩ : MeasurableSpace Ω} {mΩ' : MeasurableSpace Ω'} {P : MeasureTheory.Measure Ω} {P' : MeasureTheory.Measure Ω'} {X : ℕ → Ω → ℝ} {Y : Ω' → ℝ} [MeasureTheory.IsProbabilityMeasure P] [MeasureTheory.IsProbabilityMeasure P'] (hY : ProbabilityTheory.HasLaw Y (ProbabilityTheory.gaussianReal 0 (ProbabilityTheory.variance (X 0) P).toNNReal) P') (hX : MeasureTheory.MemLp (X 0) 2 P) (hindep : ProbabilityTheory.iIndepFun X P) (hident : ∀ (i : ℕ), ProbabilityTheory.IdentDistrib (X i) (X 0) P P) : MeasureTheory.TendstoInDistribution (fun n ω => (√↑n)⁻¹ * (∑ k ∈ Finset.range n, X k ω - ↑n * ∫ (x : Ω), X 0 x ∂P)) Filter.atTop Y (fun x => P) P' - ProbabilityTheory.condVar_bot 📋 Mathlib.Probability.CondVar
{Ω : Type u_1} {m₀ : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsProbabilityMeasure μ] (hX : AEMeasurable X μ) : ProbabilityTheory.condVar ⊥ X μ = fun _ω => ProbabilityTheory.variance X μ - ProbabilityTheory.integral_condVar_add_variance_condExp 📋 Mathlib.Probability.CondVar
{Ω : Type u_1} {m₀ m : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} (hm : m ≤ m₀) [MeasureTheory.IsProbabilityMeasure μ] (hX : MeasureTheory.MemLp X 2 μ) : ∫ (x : Ω), ProbabilityTheory.condVar m X μ x ∂μ + ProbabilityTheory.variance μ[X | m] μ = ProbabilityTheory.variance X μ - ProbabilityTheory.variance_of_ae_eq_zero_or_one 📋 Mathlib.Probability.Distributions.TwoValued
{Ω : Type u_1} {m : MeasurableSpace Ω} {X : Ω → ℝ} {μ : MeasureTheory.Measure Ω} [MeasureTheory.IsZeroOrProbabilityMeasure μ] (hXmeas : AEMeasurable X μ) (hX : ∀ᵐ (ω : Ω) ∂μ, X ω = 0 ∨ X ω = 1) : ProbabilityTheory.variance X μ = μ.real {ω | X ω = 0} * μ.real {ω | X ω = 1} - ProbabilityTheory.variance_tilted_mul 📋 Mathlib.Probability.Moments.Tilted
{Ω : Type u_1} {mΩ : MeasurableSpace Ω} {μ : MeasureTheory.Measure Ω} {X : Ω → ℝ} {t : ℝ} (ht : t ∈ interior (ProbabilityTheory.integrableExpSet X μ)) : ProbabilityTheory.variance X (μ.tilted fun x => t * X x) = iteratedDeriv 2 (ProbabilityTheory.cgf X μ) t
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