Some aspects of symmetric Gamma process mixtures

TL;DR

This paper explores symmetric Gamma process mixtures in regression, introducing a new Gibbs sampler for posterior simulation and establishing adaptive convergence rates for Gaussian mean regression.

Contribution

It presents a novel Gibbs sampling method and analyzes adaptive posterior convergence rates for symmetric Gamma process mixtures in regression.

Findings

Developed a new Gibbs sampler for symmetric Gamma process mixtures.

Established adaptive posterior convergence rates in Gaussian regression.

Enhanced understanding of Bayesian nonparametric models in regression.

Abstract

In this article, we present some specific aspects of symmetric Gamma process mixtures for use in regression models. We propose a new Gibbs sampler for simulating the posterior and we establish adaptive posterior rates of convergence related to the Gaussian mean regression problem.