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Comparison of Two Methods for Caluculating the Partition Functions of Various Spatial Statistical Models

Huang, Fuchun and Ogata, Y. 2001, Comparison of Two Methods for Caluculating the Partition Functions of Various Spatial Statistical Models, Australia and New Zealand journal of statistics, vol. 43, no. 1, pp. 47-65, doi: 10.1111/1467-842X.00154.

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Title Comparison of Two Methods for Caluculating the Partition Functions of Various Spatial Statistical Models
Author(s) Huang, Fuchun
Ogata, Y.
Journal name Australia and New Zealand journal of statistics
Volume number 43
Issue number 1
Start page 47
End page 65
Publisher Wiley-Blackwell Publishing Asia
Place of publication Richmond, Vic.
Publication date 2001
ISSN 1369-1473
Keyword(s) Gibbs sampling
likelihood
MCMC integration
metropolis algorithm
partition function
Summary 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.
Language eng
DOI 10.1111/1467-842X.00154
Field of Research 010405 Statistical Theory
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2001, Australian Statistical Publishing Association Inc.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30001401

Document type: Journal Article
Collection: School of Information Technology
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