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Journal Articles Bayesian Analysis Year : 2018

Some Aspects of Symmetric Gamma Process Mixtures

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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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cea-01838175 , version 1 (13-07-2018)

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