This note presents a unified framework to establish Poincaré and log-Sobolev inequalities, highlighting their relevance in sampling algorithms.
We study settings in which mixture and joint distributions satisfy a Poincaré (or log-Sobolev) inequality induced by a marginal and a collection of conditional distributions that are assumed to satisfy Poincaré (or log-Sobolev, resp.) inequalities and supported over Euclidean spaces. In this note, we use the framework of $Φ$-Sobolev inequalities (Chafaï, 2004) to provide a unified approach to arriving at these inequalities in the Euclidean case. This results in a simpler proof technique for establishing these functional inequalities under certain two-scale criteria. We also discuss applications of these results to certain sampling algorithms.
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Vishwak Srinivasan (2025) studied this question.
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