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Bayesian inference using artificial augmenting regressions

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>04/2009
<mark>Journal</mark>Communications in Statistics - Theory and Methods
Issue number9
Volume38
Number of pages10
Pages (from-to)1361-1370
Publication StatusPublished
<mark>Original language</mark>English

Abstract

In this article, it is shown that many intractable problems of Bayesian inference can be cast in a form called “artificial augmenting regression” in which application of Markov Chain Monte Carlo techniques, especially Gibbs sampling with data augmentation, is rather convenient. The new techniques are illustrated using several challenging statistical problems and numerical results are presented.