On measures strongly log-concave on a subspace

2022/03/17 by Pierre Bizeul, Bizeul, Pierre · 4 citations
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Mathematics #Functional Analysis (math.FA) #Point processes and geometric inequalities #Probability (math.PR) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2203.09422

openalex publication_date 2022/03/17 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28

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.

Cited by

• Distances between non-symmetric convex bodies: optimal bounds up to polylog
• On tail probability of the covariance matrix in Eldan's stochastic localization
• Optimal mean width and metric entropy estimates for convex bodies
• Faster logconcave sampling from a cold start in high dimension

Related

• Concentration of measures supported on the cube
• On the Poincaré constant of log-concave measures
• Anti-concentration inequalities for log-concave variables on the real line
• Log concavity and concentration of Lipschitz functions on the Boolean\n hypercube
• Concentration inequalities for log-concave sequences