Invariance of Normalized Tilts Under Positive Rescaling
ProvedFunctionalInequalities.Probability.tilted_smul_pos_readablefunctional-inequalitieskls-conjecturekls-modularkls-readablelog-concave
This statement proves that normalized tilting is unchanged by a positive rescaling of the base measure.
Let be a measurable space, let be a measure on , let , and let . Using the definition Measure.tilted, the normalized tilt of equals the normalized tilt of :
This algebraic invariance isolates the normalization property of tilted measures and does not require probability or finiteness assumptions on .
Original Lean declaration header
Within namespace FunctionalInequalities. Shown in its original module context. Proof omitted.
theorem Probability.tilted_smul_pos {A : Type*} [MeasurableSpace A] (μ : Measure A)
(f : A → ℝ) {c : ℝ} (hc : 0 < c) :
(ENNReal.ofReal c • μ).tilted f = μ.tilted f
Module navigation
Module: Probability/Transport.
Next: Covariance.operator_map_continuousLinearEquiv.
Source excerpt, lines 55–57 · Frozen source 408915f.
Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Probability_Transport
open MeasureTheory Filter Topology InnerProductSpace
variable {A B : Type*}
Formal statement
theorem FunctionalInequalities.Probability.tilted_smul_pos_readable {A : Type*} [MeasurableSpace A] (μ : Measure A)
(f : A → ℝ) {c : ℝ} (hc : 0 < c) :
(ENNReal.ofReal c • μ).tilted f = μ.tilted f := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Probability/Transport.lean#L54-L70 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Probability.Transport`, declaration `FunctionalInequalities.Probability.tilted_smul_pos`; the repository is private at upload time).