Stein's method, logarithmic Sobolev and transport inequalities

TL;DR

This paper establishes new functional inequalities connecting Stein's method, logarithmic Sobolev, and transport inequalities, enhancing understanding of convergence, concentration, and entropic properties for various distributions.

Contribution

It introduces a novel class of inequalities involving the Stein kernel, entropy, and Wasserstein distance, improving classical results and extending to multidimensional and non-Gaussian distributions.

Findings

Improved logarithmic Sobolev and Talagrand inequalities for Gaussian models.

Extension of inequalities to gamma, beta, and log-concave distributions.

Relevance of inequalities to convergence, concentration, and entropic decay.

Abstract

We develop connections between Stein's approximation method, logarithmic Sobolev and transport inequalities by introducing a new class of functional inequalities involving the relative entropy, the Stein kernel, the relative Fisher information and the Wasserstein distance with respect to a given reference distribution on $\mathbb{R}^d$. For the Gaussian model, the results improve upon the classical logarithmic Sobolev inequality and the Talagrand quadratic transportation cost inequality. Further examples of illustrations include multidimensional gamma distributions, beta distributions, as well as families of log-concave densities. As a by-product, the new inequalities are shown to be relevant towards convergence to equilibrium, concentration inequalities and entropic convergence expressed in terms of the Stein kernel. The tools rely on semigroup interpolation and bounds, in particular by means of the iterated gradients of the Markov generator with invariant measure the distribution under consideration. In a second part, motivated by the recent investigation by Nourdin, Peccati and Swan on Wiener chaoses, we address the issue of entropic bounds on multidimensional functionals $F$ with the Stein kernel via a set of data on $F$ and its gradients rather than on the Fisher information of the density. A natural framework for this investigation is given by the Markov Triple structure $(E, \mu, \Gamma)$ in which abstract Malliavin-type arguments may be developed and extend the Wiener chaos setting.