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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. Titterington and A. Dunmur, "Computational Bayesian Analysis of Hidden Markov Mesh Models," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 19, no. , pp. 1296-1300, 1997.
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