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Journal of Computational and Graphical Statistics Volume 18, 2009 - Issue 2
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Bayesian Computation

Bayesian Inference in Hidden Markov Random Fields for Binary Data Defined on Large Lattices

N. Friel N. Friel is Associate Professor, School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland . A. N. Pettitt is Professor, School of Mathematical Sciences, Queensland University of Technology, Australia. R. Reeves is Lecturer, School of Mathematical Sciences, Queensland University of Technology, Australia. E. Wit is Professor, Institute for Mathematics and Computer Science, University of Groningen, The Netherlands.
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A. N. Pettitt N. Friel is Associate Professor, School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland . A. N. Pettitt is Professor, School of Mathematical Sciences, Queensland University of Technology, Australia. R. Reeves is Lecturer, School of Mathematical Sciences, Queensland University of Technology, Australia. E. Wit is Professor, Institute for Mathematics and Computer Science, University of Groningen, The Netherlands.
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R. Reeves N. Friel is Associate Professor, School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland . A. N. Pettitt is Professor, School of Mathematical Sciences, Queensland University of Technology, Australia. R. Reeves is Lecturer, School of Mathematical Sciences, Queensland University of Technology, Australia. E. Wit is Professor, Institute for Mathematics and Computer Science, University of Groningen, The Netherlands.
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E. Wit N. Friel is Associate Professor, School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland . A. N. Pettitt is Professor, School of Mathematical Sciences, Queensland University of Technology, Australia. R. Reeves is Lecturer, School of Mathematical Sciences, Queensland University of Technology, Australia. E. Wit is Professor, Institute for Mathematics and Computer Science, University of Groningen, The Netherlands.
Pages 243-261 | Received 01 Nov 2006, Published online: 01 Jan 2012

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Luigi Spezia, Nial Friel & Alessandro Gimona. (2018) Spatial hidden Markov models and species distributions. Journal of Applied Statistics 45:9, pages 1595-1615.
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Wanchuang Zhu & Yanan Fan. (2018) A Novel Approach for Markov Random Field With Intractable Normalizing Constant on Large Lattices. Journal of Computational and Graphical Statistics 27:1, pages 59-70.
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Lionel Cucala & Jean-michel Marin. (2013) Bayesian Inference on a Mixture Model With Spatial Dependence. Journal of Computational and Graphical Statistics 22:3, pages 584-597.
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Nial Friel. (2013) Evidence and Bayes Factor Estimation for Gibbs Random Fields. Journal of Computational and Graphical Statistics 22:3, pages 518-532.
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Donatello Telesca, Peter Müller, StevenM. Kornblau, MarcA. Suchard & Yuan Ji. (2012) Modeling Protein Expression and Protein Signaling Pathways. Journal of the American Statistical Association 107:500, pages 1372-1384.
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RichardG. Everitt. (2012) Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks. Journal of Computational and Graphical Statistics 21:4, pages 940-960.
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Håkon Tjelmeland & Haakon Michael Austad. (2012) Exact and Approximate Recursive Calculations for Binary Markov Random Fields Defined on Graphs. Journal of Computational and Graphical Statistics 21:3, pages 758-780.
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ClareA. McGrory, AnthonyN. Pettitt, Robert Reeves, Mark Griffin & Mark Dwyer. (2012) Variational Bayes and the Reduced Dependence Approximation for the Autologistic Model on an Irregular Grid With Applications. Journal of Computational and Graphical Statistics 21:3, pages 781-796.
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Articles from other publishers (28)

Wanchuang Zhu & Yanan Fan. (2022) A synthetic likelihood approach for intractable markov random fields. Computational Statistics 38:2, pages 749-777.
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Francesco Bartolucci & Alessio Farcomeni. (2022) A spatio-temporal model based on discrete latent variables for the analysis of COVID-19 incidence. Spatial Statistics 49, pages 100504.
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Francesco Bartolucci & Alessio Farcomeni. (2022) A Hidden Markov Space–Time Model for Mapping the Dynamics of Global Access to Food. Journal of the Royal Statistical Society Series A: Statistics in Society 185:1, pages 246-266.
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Matthew T. Moores, Anthony N. Pettitt & Kerrie L. Mengersen. 2020. Case Studies in Applied Bayesian Data Science. Case Studies in Applied Bayesian Data Science 137 151 .
Cornelia Vacar & Jean-François Giovannelli. (2019) Unsupervised joint deconvolution and segmentation method for textured images: a Bayesian approach and an advanced sampling algorithm. EURASIP Journal on Advances in Signal Processing 2019:1.
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Dong Liu, Nima N. Moghadam, Lars K. Rasmussen, Jinliang Huang & Saikat Chatterjee. (2019) α Belief Propagation as Fully Factorized Approximation. α Belief Propagation as Fully Factorized Approximation.
N. Peyrard, M.‐J. Cros, S. Givry, A. Franc, S. Robin, R. Sabbadin, T. Schiex & M. Vignes. (2019) Exact or approximate inference in graphical models: why the choice is dictated by the treewidth, and how variable elimination can be exploited. Australian & New Zealand Journal of Statistics 61:2, pages 89-133.
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Luigi Spezia. (2018) Modelling covariance matrices by the trigonometric separation strategy with application to hidden Markov models. TEST 28:2, pages 399-422.
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Xin Luo & Håkon Tjelmeland. (2018) Prior specification for binary Markov mesh models. Statistics and Computing 29:2, pages 367-389.
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Haakon Michael Austad & Håkon Tjelmeland. (2016) Approximate computations for binary Markov random fields and their use in Bayesian models. Statistics and Computing 27:5, pages 1271-1292.
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Lampros Bouranis, Nial Friel & Florian Maire. (2017) Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution. Social Networks 50, pages 98-108.
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Petter Arnesen & Håkon Tjelmeland. (2016) Prior specification of neighbourhood and interaction structure in binary Markov random fields. Statistics and Computing 27:3, pages 737-756.
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H. E. Ogden. (2017) On asymptotic validity of naive inference with an approximate likelihood. Biometrika 104:1, pages 153-164.
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Luigi SpeziaMark J. BrewerChristian Birkel. (2017) An anisotropic and inhomogeneous hidden Markov model for the classification of water quality spatio-temporal series on a national scale: The case of Scotland. Environmetrics 28:1, pages e2427.
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P. Alquier, N. Friel, R. Everitt & A. Boland. (2014) Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels. Statistics and Computing 26:1-2, pages 29-47.
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Julien Stoehr, Jean-Michel Marin & Pierre Pudlo. (2016) Hidden Gibbs random fields model selection using Block Likelihood Information Criterion. Stat 5:1, pages 158-172.
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Petter Arnesen & Håkon Tjelmeland. (2015) Fully Bayesian Binary Markov Random Field Models: Prior Specification and Posterior Simulation. Scandinavian Journal of Statistics 42:4, pages 967-987.
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Anne-Marie Lyne, Mark Girolami, Yves Atchadé, Heiko Strathmann & Daniel Simpson. (2015) On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods. Statistical Science 30:4.
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Julien Stoehr, Pierre Pudlo & Lionel Cucala. (2014) Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields. Statistics and Computing 25:1, pages 129-141.
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Petruţa C. Caragea & Emily Berg. (2014) A Centered Bivariate Spatial Regression Model for Binary Data with an Application to Presettlement Vegetation Data in the Midwestern United States. Journal of Agricultural, Biological, and Environmental Statistics 19:4, pages 451-469.
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Matthew Walker & Andrew Curtis. (2014) Spatial Bayesian inversion with localized likelihoods: An exact sampling alternative to MCMC. Journal of Geophysical Research: Solid Earth 119:7, pages 5741-5761.
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Ick Hoon Jin & Faming Liang. (2014) Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants. Computational Statistics & Data Analysis 71, pages 402-416.
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Solaiman Afroughi, Mehdi Ghandehari Motlagh, Soghrat Faghihzadeh & Majid Jafari Khaledi. (2013) A model for analyzing spatially correlated binary data clustered in uncorrelated lattices. Statistical Methodology 14, pages 1-14.
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Rao Fu, Andrew L. Thurman, Tingjin Chu, Michelle M. Steen-Adams & Jun Zhu. (2013) On Estimation and Selection of Autologistic Regression Models via Penalized Pseudolikelihood. Journal of Agricultural, Biological, and Environmental Statistics 18:3, pages 429-449.
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Håkon Toftaker & Håkon Tjelmeland. (2013) Construction of Binary Multi-grid Markov Random Field Prior Models from Training Images. Mathematical Geosciences 45:4, pages 383-409.
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Nial Friel. (2012) Bayesian inference for Gibbs random fields using composite likelihoods. Bayesian inference for Gibbs random fields using composite likelihoods.
Alberto Caimo & Nial Friel. (2011) Bayesian inference for exponential random graph models. Social Networks 33:1, pages 41-55.
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C. A. McGrory, D. M. Titterington, R. Reeves & A. N. Pettitt. (2008) Variational Bayes for estimating the parameters of a hidden Potts model. Statistics and Computing 19:3, pages 329-340.
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