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

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

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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.

Item Type: Article
Uncontrolled Keywords: gibbs sampling, likelihood, MCMC integration, metropolis algorithm, partition function
Subjects: RFCD Classification > 290000 Engineering and Technology
Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: Ms Phung T Tran
Date Deposited: 04 Feb 2009 13:24
Last Modified: 06 Jul 2011 00:53
URI: http://vuir.vu.edu.au/id/eprint/1725
DOI: 10.1111/1467-842X.00154
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Citations in Scopus: 7 - View on Scopus

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