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Developing satisfactory methodology for the analysis of Markov random field is a very challenging task.
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A consistent model selection procedure for Markov random fields based on penalized pseudolikelihood
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Consistent Estimation of the Basic Neighborhood of Markov Random Fields
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Bayesian Clustering Using Hidden Markov Random Fields in Spatial Population Genetics
O. François, S. Ancelet, and G. Guillot · 2006
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An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
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MCMC for doubly-intractable distributions
I. Murray, Z. Ghahramani, and D. J. C. MacKay · 2006
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Combining Monte Carlo and Mean Field-Like Methods for Inference in Hidden Markov Random Fields
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Exact Sampling with Coupled Markov chains and Applications to Statistical Mechanics
J. G. Propp and D. B. Wilson · 1996
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Approximate Bayes model selection procedures for Gibbs-Markov random fields
L. Seymour and C. Ji · 1996
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Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p ∗ p^{\ast}
S. Wasserman and P. Pattison · 1996
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Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
A. Gelman and X.-L. Meng · 1998
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Auxiliary variable methods for Markov chain Monte Carlo with applications
D. M. Higdon · 1998
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Computational Bayesian analysis of hidden Markov models
T. Rydén and D. Titterington · 1998
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F. Forbes and G. Fort · 2007
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Recursive computing and simulation-free inference for general factorizable models
N. Friel and H. Rue · 2007
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An introduction to exponential random graph (p*) models for social networks
G. Robins, P. Pattison, Y. Kalish, and D. Lusher · 2007
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A finite mixture model for image segmentation
M. Alfò, L. Nieddu, and D. Vicari · 2008
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The Pseudo-Marginal Approach for Efficient Monte Carlo Computations
C. Andrieu and G. O. Roberts · 2009
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A Bayesian Reassessment of Nearest-Neighbor Classification
L. Cucala, J.-M. Marin, C. P. Robert, and D. M. Titterington · 2009
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Bayesian Inference in Hidden Markov Random Fields for Binary Data Defined on Large Lattices
N. Friel, A. N. Pettitt, R. Reeves, and E. Wit · 2009
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ABC likelihood-free methods for model choice in Gibbs random fields
A. Grelaud, C. P. Robert, J.-M. Marin, F. Rodolphe, and J.-F. Taly · 2009
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Variational Bayes for estimating the parameters of a hidden Potts model
C. A. McGrory, D. M. Titterington, R. Reeves, and A. N. Pettitt · 2009
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Particle Markov Chain Monte Carlo methods
C. Andrieu, A. Doucet, and R. Holenstein · 2010
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Introduction to Automatic Differentiation and MATLAB Object-Oriented Programming
R. D. Neidinger · 2010
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Bayesian inference for exponential random graph models
A. Caimo and N. Friel · 2011
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Likelihood-free estimation of model evidence
X. Didelot, R. G. Everitt, A. M. Johansen, and D. J. Lawson · 2011
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Gibbs Measures and Phase Transitions
H. Georgii · 2011
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Lack of confidence in approximate Bayesian computation model choice
C. P. Robert, J.-M. Cornuet, J.-M. Marin, and N. S. Pillai · 2011
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An overview of composite likelihood methods
C. Varin, N. Reid, and D. Firth · 2011
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Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks
R. G. Everitt · 2012
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Approximate Bayesian Computational methods
J.-M. Marin, P. Pudlo, C. P. Robert, and R. J. Ryder · 2012
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Bayesian Inference on a Mixture Model With Spatial Dependence
L. Cucala and J.-M. Marin · 2013
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Relevant statistics for Bayesian model choice
J.-M. Marin, N. S. Pillai, C. P. Robert, and J. Rousseau · 2014
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Segmentation of cone-beam CT using a hidden Markov random field with informative priors
M. T. Moores, C. E. Hargrave, F. Harden, and K. Mengersen · 2014
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Semi-automatic selection of summary statistics for ABC model choice
D. Prangle, P. Fearnhead, M. P. Cox, P. J. Biggs, and N. P. French · 2014
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On Russian Roulette Estimates for Bayesian Inference with Doubly-Intractable Likelihoods
A.-M. Lyne, M. Girolami, Y. Atchadé, H. Strathmann, and D. Simpson · 2015
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Reliable abc model choice via random forests
P. Pudlo, J.-M. Marin, A. Estoup, J.-M. Cornuet, M. Gauthier, and C. P. Robert · 2015
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Adaptive ABC model choice and geometric summary statistics for hidden Gibbs random fields
J. Stoehr, P. Pudlo, and L. Cucala · 2015
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Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels
P. Alquier, N. Friel, R. Everitt, and A. Boland · 2016
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Hidden Gibbs random fields model selection using Block Likelihood Information Criterion
J. Stoehr, J.-M. Marin, and P. Pudlo · 2016
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