Reweighted information inequalities

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1. Verfasser: Niles-Weed, Jonathan
Format: Preprint
Veröffentlicht: 2026
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author Niles-Weed, Jonathan
author_facet Niles-Weed, Jonathan
contents We establish a variant of the log-Sobolev and transport-information inequalities for mixture distributions. If a probability measure $π$ can be decomposed into components that individually satisfy such inequalities, then any measure $μ$ close to $π$ in relative Fisher information is close in relative entropy or transport distance to a reweighted version of $π$ with the same mixture components but possibly different weights. This provides a user-friendly interpretation of Fisher information bounds for non-log-concave measures and explains phenomena observed in the analysis of Langevin Monte Carlo for multimodal distributions.
format Preprint
id arxiv_https___arxiv_org_abs_2603_13135
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Reweighted information inequalities
Niles-Weed, Jonathan
Information Theory
Probability
60E15, 47D07
We establish a variant of the log-Sobolev and transport-information inequalities for mixture distributions. If a probability measure $π$ can be decomposed into components that individually satisfy such inequalities, then any measure $μ$ close to $π$ in relative Fisher information is close in relative entropy or transport distance to a reweighted version of $π$ with the same mixture components but possibly different weights. This provides a user-friendly interpretation of Fisher information bounds for non-log-concave measures and explains phenomena observed in the analysis of Langevin Monte Carlo for multimodal distributions.
title Reweighted information inequalities
topic Information Theory
Probability
60E15, 47D07
url https://arxiv.org/abs/2603.13135