The Generalized Gibbs Sampler and the Neighborhood Sampler

Jonathan Keith (), George Sofronov () and Dirk Kroese ()
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Jonathan Keith: Queensland University of Technology, School of Mathematical Sciences
George Sofronov: University of Queensland, Department of Mathematics
Dirk Kroese: University of Queensland, Department of Mathematics

A chapter in Monte Carlo and Quasi-Monte Carlo Methods 2006, 2008, pp 537-547 from Springer

Abstract: The Generalized Gibbs Sampler (GGS) is a recently proposed Markov chain Monte Carlo (MCMC) technique that is particularly useful for sampling from distributions defined on spaces in which the dimension varies from point to point or in which points are not easily defined in terms of co-ordinates. Such spaces arise in problems involving model selection and model averaging and in a number of interesting problems in computational biology. Such problems have hitherto been the domain of the Reversible-jump Sampler, but the method described here, which generalizes the well-known conventional Gibbs Sampler, provides an alternative that is easy to implement and often highly efficient.

Keywords: Markov Chain; Markov Chain Monte Carlo; Transition Matrix; Markov Chain Monte Carlo Method; Target Distribution (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-74496-2_31

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DOI: 10.1007/978-3-540-74496-2_31

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