Full Affine Support of the Intrinsic Measure
ProvedFunctionalInequalities.Probability.affineSupport_intrinsicMeasure_readableThis establishes the geometric fullness of intrinsic coordinates.
Let be a finite-dimensional real inner-product space with its Borel measurable structure and second-countable topology, and let be any probability measure on . Let be the direction space of the affine support of , and let intrinsicMeasure μ be the corresponding intrinsic probability measure on . Then
The statement is purely geometric: the intrinsic construction removes redundant affine directions and leaves a law with full affine support.
Formalization Note No second-moment, log-concavity, density, or absolute-continuity hypothesis is assumed; completeness is omitted from the declaration's required assumptions.
Original Lean declaration header
Within namespace FunctionalInequalities.Probability. Shown in its original module context. Proof omitted.
theorem affineSupport_intrinsicMeasure (μ : ProbabilityMeasure E) :
affineSupport (intrinsicMeasure μ) = ⊤
Module navigation
Module: Probability/AffineSupport.
Source excerpt, lines 218–219 · Frozen source 408915f.
import Definitions.Def_KLSModular_FunctionalInequalities_Probability_AffineSupport
open MeasureTheory
universe u
variable {E : Type u} [NormedAddCommGroup E] [InnerProductSpace ℝ E]
[MeasurableSpace E]
variable [CompleteSpace E] [BorelSpace E] [SecondCountableTopology E] [FiniteDimensional ℝ E]
omit [CompleteSpace E]
theorem FunctionalInequalities.Probability.affineSupport_intrinsicMeasure_readable (μ : ProbabilityMeasure E) :
affineSupport (intrinsicMeasure μ) = ⊤ := by sorry