PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection
Silvia Pandolfi, Francesco Bartolucci and Nial Friel
JMLR Workshop and Conference Proceedings Volume 9, pp. 581-588, 2010. ISSN 1533-7928

Abstract

We propose a generalization of the Multiple-try Metropolis (MTM) algorithm of Liu et al. (2000), which is based on drawing several proposals at each step and randomly choosing one of them on the basis of weights that may be arbitrary chosen. In particular, for Bayesian estimation we also introduce a method based on weights depending on a quadratic approximation of the posterior distribution. The resulting algorithm cannot be reformulated as an MTM algorithm and leads to a comparable gain of efficiency with a lower computational effort. We also outline the extension of the proposed strategy, and then of the MTM strategy, to Bayesian model selection, casting it in a Reversible Jump framework. The approach is illustrated by real examples.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7723
Deposited By:Nial Friel
Deposited On:17 March 2011