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LeanPool.Feige.Grunbaum.ProbabilityCore

Probability lemmas for Grünbaum's inequality #

The cumulative distribution function regarded as a map into [0,1].

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    theorem Grunbaum.Integrable.integral_eq_integral_Ioc_meas_lt' {α : Type u_1} [MeasurableSpace α] {μ : MeasureTheory.Measure α} {f : α} {M : } (f_intble : MeasureTheory.Integrable f μ) (f_nn : 0 ≤ᵐ[μ] f) (f_bdd : f ≤ᵐ[μ] fun (x : α) => M) :
    (ω : α), f ω μ = (t : ) in Set.Ioc 0 M, μ.real {a : α | t < f a}
    theorem Grunbaum.cdf_rpow_inv_natCast_le_at_mean (μ : MeasureTheory.Measure ) [MeasureTheory.IsProbabilityMeasure μ] [MeasureTheory.NullSingletonClass μ] {n : } (hn : n 0) (m : ) (hconc : ConcaveOn (Set.Ici m) fun (x : ) => (ProbabilityTheory.cdf μ) x ^ (↑n)⁻¹) (hsupport : ∀ᵐ (x : ) μ, x Set.Ici m) (hid : MeasureTheory.Integrable (fun (x : ) => x) μ) :
    n / (n + 1) (ProbabilityTheory.cdf μ) ( (x : ), x μ) ^ (↑n)⁻¹

    Lebesgue volume restricted to K and normalized to total mass one.

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      The centroid of K defined by its set average.

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        theorem Grunbaum.ContinuousLinearMap.map_setAverage {α : Type u_1} {E : Type u_2} {F : Type u_3} [MeasurableSpace α] [NormedAddCommGroup E] [NormedSpace E] [CompleteSpace E] [NormedAddCommGroup F] [NormedSpace F] [CompleteSpace F] (L : E →L[] F) (μ : MeasureTheory.Measure α) (s : Set α) (f : αE) (hf : MeasureTheory.IntegrableOn f s μ) :
        L ( (x : α) in s, f x μ) = (x : α) in s, L (f x) μ