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Comparison of Two Methods for Calculating the Partition Functions of Various Spatial Statistical Models
Author(s) -
Huang Fuchun,
Ogata Yosihiko
Publication year - 2001
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/1467-842x.00154
Subject(s) - mathematics , markov chain , partition function (quantum field theory) , ising model , markov chain monte carlo , statistical physics , gaussian , monte carlo method , computation , statistics , algorithm , physics , quantum mechanics
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.