Corpus ID: 11292761

Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields

  title={Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields},
  author={Julien Stoehr and Nial Friel},
Gibbs random elds play an important role in statistics, however, the resulting likelihood is typically unavailable due to an intractable normalizing constant. Composite likelihoods oer a principled means to construct useful approximations. This paper provides a mean to calibrate the posterior distribution resulting from using a composite likelihood and illustrate its performance in several examples. 
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Bayesian inference for Gibbs random fields using composite likelihoods
  • N. Friel
  • Computer Science, Mathematics
  • Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)
  • 2012
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