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

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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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Keywords: Gibbs sampling; MCMC integration; Metropolis algorithm; likelihood; partition function

Document Type: Research Article

Affiliations: 1: School of Computing and Mathematics, Deakin University, Australia, 2: Dept of Statistical Science, The Graduate University for Advanced Studies, Japan, and The Institute of Statistical Mathematics, Japan

Publication date: March 1, 2001

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