A generalized multiple-try version of the Reversible Jump algorithm

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Title: A generalized multiple-try version of the Reversible Jump algorithm
Authors: Pandolfi, Slivia
Bartolucci, Francesco
Friel, Nial
Permanent link: http://hdl.handle.net/10197/8372
Date: Apr-2014
Abstract: The Reversible Jump algorithm is one of the most widely used Markov chain Monte Carlo algorithms for Bayesian estimation and model selection. A generalized multiple-try version of this algorithm is proposed. The algorithm is based on drawing several proposals at each step and randomly choosing one of them on the basis of weights (selection probabilities) that may be arbitrarily chosen. Among the possible choices, a method is employed which is based on selection probabilities depending on a quadratic approximation of the posterior distribution. Moreover, the implementation of the proposed algorithm for challenging model selection problems, in which the quadratic approximation is not feasible, is considered. The resulting algorithm leads to a gain in efficiency with respect to the Reversible Jump algorithm, and also in terms of computational effort. The performance of this approach is illustrated for real examples involving a logistic regression model and a latent class model.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Elsevier
Copyright (published version): 2013 Elsevier
Keywords: Machine learningStatisticsBayesian inferenceLatent class modelLogistic modelMarkov chain Monte CarloMetropolis–Hastings algorithm
DOI: 10.1016/j.csda.2013.10.007
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection
Insight Research Collection

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