Positive Normalizing Integral for a Probability Tilt
ProvedFunctionalInequalities.KLS.LogConcave.integral_exp_neg_pos_of_eq_tilted_readableThis is a positivity statement for a tilted probability law.
Let be a measurable space, let be a probability measure on , let be a measure on , and let . Assume that as measures. Then the normalizing integral is strictly positive:
The result is a basic normalization fact used when identifying densities of tilted probability measures; it does not require norm, inner-product, finite-dimensional, or Borel-space structure.
Original Lean declaration header
Within namespace FunctionalInequalities.KLS.LogConcave. Shown in its original module context. Proof omitted.
theorem integral_exp_neg_pos_of_eq_tilted {m : Measure V} {W : V → ℝ}
(hνW : (ν : Measure V) = m.tilted (-W)) :
0 < ∫ x, Real.exp (-W x) ∂m
Module navigation
Module: KLS/LogConcave/Density/Tilted.
Supporting theorems: integrable_exp_neg_of_eq_tilted.
Previous: integrable_exp_neg_of_eq_tilted · Next: rpow_mul_rpow_le_of_convexOn.
Source excerpt, lines 49–51 · Frozen source 408915f.
import Definitions.Def_KLSModular_FunctionalInequalities_KLS_LogConcave_Density_Tilted
open MeasureTheory Set
variable {V : Type*} [NormedAddCommGroup V] [InnerProductSpace ℝ V] [FiniteDimensional ℝ V]
[MeasurableSpace V] [BorelSpace V] {ν : MeasureTheory.ProbabilityMeasure V}
omit [NormedAddCommGroup V] [InnerProductSpace ℝ V] [FiniteDimensional ℝ V] [BorelSpace V]
theorem FunctionalInequalities.KLS.LogConcave.integral_exp_neg_pos_of_eq_tilted_readable {m : Measure V} {W : V → ℝ}
(hνW : (ν : Measure V) = m.tilted (-W)) :
0 < ∫ x, Real.exp (-W x) ∂m := by sorry