On measures strongly log-concave on a subspace
Pierre Bizeul
TL;DR
This paper investigates the concentration properties of measures that are strongly log-concave only on a subspace, using an adapted stochastic localization process to analyze their behavior.
Contribution
It introduces a novel approach to study measures with partial strong log-concavity on a subspace, extending existing concentration results.
Findings
Establishes concentration inequalities for measures curved on a subspace
Develops an adapted stochastic localization technique
Provides theoretical insights into partial log-concavity
Abstract
In this work we study the concentration properties of log-concave measures that are curved only on a subspace of directions. Proofs uses an adapted version of the stochastic localization process.
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Taxonomy
TopicsBayesian Methods and Mixture Models · Point processes and geometric inequalities · Statistical Methods and Inference