Variational Bayes for estimating the parameters of a hidden Potts model

  title={Variational Bayes for estimating the parameters of a hidden Potts model},
  author={Clare A. McGrory and D. M. Titterington and R. Reeves and Anthony N. Pettitt},
  journal={Statistics and Computing},
Hidden Markov random field models provide an appealing representation of images and other spatial problems. The drawback is that inference is not straightforward for these models as the normalisation constant for the likelihood is generally intractable except for very small observation sets. Variational methods are an emerging tool for Bayesian inference and they have already been successfully applied in other contexts. Focusing on the particular case of a hidden Potts model with Gaussian noise… CONTINUE READING
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Bayesian analysis of hidden Markov models using variational approximations

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