Probability lemmas for Grünbaum's inequality #
theorem
Grunbaum.volume_preimage_singleton_eq_zero
{E : Type u_1}
[NormedAddCommGroup E]
[NormedSpace ℝ E]
[FiniteDimensional ℝ E]
[MeasureTheory.MeasureSpace E]
[BorelSpace E]
[MeasureTheory.volume.IsAddHaarMeasure]
(L : E →L[ℝ] ℝ)
(hL : L ≠ 0)
(t : ℝ)
:
theorem
Grunbaum.nullSingletonClass_map_uniform
{E : Type u_1}
[NormedAddCommGroup E]
[NormedSpace ℝ E]
[FiniteDimensional ℝ E]
[MeasureTheory.MeasureSpace E]
[BorelSpace E]
[MeasureTheory.volume.IsAddHaarMeasure]
(K : Set E)
(L : E →L[ℝ] ℝ)
(hL : L ≠ 0)
:
theorem
Grunbaum.measure_cdf_preimage_Iic
(μ : MeasureTheory.Measure ℝ)
[MeasureTheory.IsProbabilityMeasure μ]
[MeasureTheory.NullSingletonClass μ]
(u : ℝ)
(hu0 : 0 ≤ u)
(hu1 : u < 1)
:
The cumulative distribution function regarded as a map into [0,1].
Equations
- Grunbaum.cdfToUnitInterval μ x = ⟨↑(ProbabilityTheory.cdf μ) x, ⋯⟩
Instances For
theorem
Grunbaum.map_cdfToUnitInterval_apply_Iic
(μ : MeasureTheory.Measure ℝ)
[MeasureTheory.IsProbabilityMeasure μ]
[MeasureTheory.NullSingletonClass μ]
(u : ↑unitInterval)
:
theorem
Grunbaum.Integrable.integral_eq_integral_Ioc_meas_lt'
{α : Type u_1}
[MeasurableSpace α]
{μ : MeasureTheory.Measure α}
{f : α → ℝ}
{M : ℝ}
(f_intble : MeasureTheory.Integrable f μ)
(f_nn : 0 ≤ᵐ[μ] f)
(f_bdd : f ≤ᵐ[μ] fun (x : α) => M)
:
theorem
Grunbaum.integral_cdf_rpow_inv_natCast
(μ : MeasureTheory.Measure ℝ)
[MeasureTheory.IsProbabilityMeasure μ]
[MeasureTheory.NullSingletonClass μ]
{n : ℕ}
(hn : n ≠ 0)
:
theorem
Grunbaum.cdf_rpow_inv_natCast_le_at_mean
(μ : MeasureTheory.Measure ℝ)
[MeasureTheory.IsProbabilityMeasure μ]
[MeasureTheory.NullSingletonClass μ]
{n : ℕ}
(hn : n ≠ 0)
(m : ℝ)
(hconc : ConcaveOn ℝ (Set.Ici m) fun (x : ℝ) => ↑(ProbabilityTheory.cdf μ) x ^ (↑n)⁻¹)
(hsupport : ∀ᵐ (x : ℝ) ∂μ, x ∈ Set.Ici m)
(hid : MeasureTheory.Integrable (fun (x : ℝ) => x) μ)
:
noncomputable def
Grunbaum.uniformVolume
{E : Type u_1}
[MeasureTheory.MeasureSpace E]
(K : Set E)
:
Lebesgue volume restricted to K and normalized to total mass one.
Equations
Instances For
noncomputable def
Grunbaum.volumeCentroid
{E : Type u_1}
[NormedAddCommGroup E]
[NormedSpace ℝ E]
[MeasureTheory.MeasureSpace E]
(K : Set E)
:
E
The centroid of K defined by its set average.
Equations
- Grunbaum.volumeCentroid K = ⨍ (x : E) in K, x
Instances For
theorem
Grunbaum.isProbabilityMeasure_uniformVolume
{E : Type u_1}
[MeasureTheory.MeasureSpace E]
(K : Set E)
(hK0 : MeasureTheory.volume K ≠ 0)
(hKtop : MeasureTheory.volume K ≠ ⊤)
:
theorem
Grunbaum.volumeCentroid_eq_integral_uniformVolume
{E : Type u_1}
[NormedAddCommGroup E]
[NormedSpace ℝ E]
[MeasureTheory.MeasureSpace E]
(K : Set E)
:
theorem
Grunbaum.integral_id_map_uniformVolume_eq_centroid_projection
{E : Type u_1}
[NormedAddCommGroup E]
[NormedSpace ℝ E]
[FiniteDimensional ℝ E]
[MeasureTheory.MeasureSpace E]
[BorelSpace E]
[MeasureTheory.volume.IsAddHaarMeasure]
(K : Set E)
(hK : IsCompact K)
(L : E →L[ℝ] ℝ)
:
theorem
Grunbaum.ContinuousLinearMap.map_setAverage
{α : Type u_1}
{E : Type u_2}
{F : Type u_3}
[MeasurableSpace α]
[NormedAddCommGroup E]
[NormedSpace ℝ E]
[CompleteSpace E]
[NormedAddCommGroup F]
[NormedSpace ℝ F]
[CompleteSpace F]
(L : E →L[ℝ] F)
(μ : MeasureTheory.Measure α)
(s : Set α)
(f : α → E)
(hf : MeasureTheory.IntegrableOn f s μ)
: