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An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants

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Author Info
J. Møller
A. N. Pettitt
R. Reeves
K. K. Berthelsen
Abstract

Maximum likelihood parameter estimation and sampling from Bayesian posterior distributions are problematic when the probability density for the parameter of interest involves an intractable normalising constant which is also a function of that parameter. In this paper, an auxiliary variable method is presented which requires only that independent samples can be drawn from the unnormalised density at any particular parameter value. The proposal distribution is constructed so that the normalising constant cancels from the Metropolis-Hastings ratio. The method is illustrated by producing posterior samples for parameters of the Ising model given a particular lattice realisation. Copyright 2006, Oxford University Press.

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File URL: http://hdl.handle.net/10.1093/biomet/93.2.451
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Article provided by Oxford University Press for Biometrika Trust in its journal Biometrika.

Volume (Year): 93 (2006)
Issue (Month): 2 (June)
Pages: 451-458
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Handle: RePEc:oup:biomet:v:93:y:2006:i:2:p:451-458

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  1. Marco Del Negro & Frank Schorfheide, 2008. "Forming Priors for DSGE Models (and How it Affects the Assessment of Nominal Rigidities)," NBER Working Papers 13741, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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