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<p><b>Abstract</b>—Versions of the Gibbs Sampler are derived for the analysis of data from the hidden Markov Mesh random fields sometimes used in image analysis. This provides a numerical approach to the otherwise intractable Bayesian analysis of these problems. Detailed formulation is provided for particular examples based on Devijver's [<ref rid="bibi12964" type="bib">4</ref>] Markov Mesh model, and the <tt>BUGS</tt> [<ref rid="bibi129620" type="bib">20</ref>] package is used to do the computations. Theoretical aspects are discussed and a numerical study, based on image analysis, is reported.</p>
Bayesian inference, Gibbs sampling, hidden Markov Mesh random field, Markov chain Monte Carlo.
D.m. Titterington, A.p. Dunmur, "Computational Bayesian Analysis of Hidden Markov Mesh Models", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 19, no. , pp. 1296-1300, November 1997, doi:10.1109/34.632989
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