In this article, we present some specific aspects of symmetric Gamma process mixtures for use in regression models. First we propose a new Gibbs sampler for simulating the posterior. The algorithm is tested on two examples, the mean regression problem with normal errors, and the reconstruction of two dimensional CT images. In a second time, we establish posterior rates of convergence related to the mean regression problem with normal errors. For location-scale and location-modulation mixtures the rates are adaptive over Hölder classes, and in the case of location-modulation mixtures are nearly optimal.
"Some Aspects of Symmetric Gamma Process Mixtures." Bayesian Anal. 13 (3) 703 - 720, September 2018. https://doi.org/10.1214/17-BA1058