Skip to Main content Skip to Navigation
Journal articles

Some Aspects of Symmetric Gamma Process Mixtures

Zacharie Naulet 1 Eric Barat 1
1 LM2S - Laboratoire Modélisation et Simulation de Systèmes
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
Abstract : 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 Holder classes, and in the case of location-modulation mixtures are nearly optimal.
Complete list of metadatas

Cited literature [25 references]  Display  Hide  Download

https://hal-cea.archives-ouvertes.fr/cea-01838175
Contributor : Marie-France Robbe <>
Submitted on : Friday, July 13, 2018 - 10:38:54 AM
Last modification on : Monday, February 10, 2020 - 6:14:09 PM
Long-term archiving on: : Monday, October 15, 2018 - 8:13:40 PM

File

Openaccess_euclid.ba.150734164...
Files produced by the author(s)

Identifiers

Collections

Citation

Zacharie Naulet, Eric Barat. Some Aspects of Symmetric Gamma Process Mixtures. Bayesian Analysis, International Society for Bayesian Analysis, 2018, 13, pp.703 - 720. ⟨10.1214/17-BA1058⟩. ⟨cea-01838175⟩

Share

Metrics

Record views

164

Files downloads

226