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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.
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Submitted on : Friday, July 13, 2018 - 10:38:54 AM
Last modification on : Thursday, February 17, 2022 - 10:08:05 AM
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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⟩



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