Comparison of two methods for calculating the partition functions of various spatial statistical models

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Huang, Fuchun and Ogata, Yosihiko (2001) Comparison of two methods for calculating the partition functions of various spatial statistical models. Australian & New Zealand Journal of Statistics, 43 (1). pp. 47-65. ISSN 1369-1473

Abstract

Likelihood computation in spatial statistics requires accurate and efficient calculation of the normalizing constant (i.e. partition function) of the Gibbs distribution of the model. Two available methods to calculate the normalizing constant by Markov chain Monte Carlo methods are compared by simulation experiments for an Ising model, a Gaussian Markov field model and a pairwise interaction point field model.

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Item type	Article
URI	https://vuir.vu.edu.au/id/eprint/1725
DOI	10.1111/1467-842X.00154
Official URL	http://dx.doi.org/10.1111/1467-842X.00154
Subjects	Historical > RFCD Classification > 290000 Engineering and Technology Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords	gibbs sampling, likelihood, MCMC integration, metropolis algorithm, partition function
Citations in Scopus	10 - View on Scopus
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