Lancaster EPrints

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

Pettitt, Anthony and Berthelsen, K. and Moller, J. and Reeves, R. (2006) An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. Biometrika, 93 (2). pp. 451-458. ISSN 1464-3510

Full text not available from this repository.

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.

Item Type: Article
Journal or Publication Title: Biometrika
Additional Information: RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research
Uncontrolled Keywords: Auxiliary variable method ; Ising model ; Markov chain Monte Carlo ; Metropolis-Hastings algorithm ; Normalising constant ; Partition function
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 2435
Deposited By: ep_importer
Deposited On: 31 Mar 2008 10:01
Refereed?: Yes
Published?: Published
Last Modified: 26 Jul 2012 16:24
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/2435

Actions (login required)

View Item