On reparametrization and the Gibbs sampler

Jorge Carlos Román, James P. Hobert and Brett Presnell

Statistics & Probability Letters, 2014, vol. 91, issue C, 110-116

Abstract: Gibbs samplers derived under different parametrizations of the target density can have radically different rates of convergence. In this article, we specify conditions under which reparametrization leaves the convergence rate of a Gibbs chain unchanged. An example illustrates how these results can be exploited in convergence rate analyses.

Keywords: Markov chain; Geometric ergodicity; Monte Carlo; Convergence rate; Parametrization (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1016/j.spl.2014.03.024

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