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

@InProceedings{pmlr-v38-stoehr15,
  title = 	 {{Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields}},
  author = 	 {Stoehr, Julien and Friel, Nial},
  booktitle = 	 {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics},
  pages = 	 {921--929},
  year = 	 {2015},
  editor = 	 {Lebanon, Guy and Vishwanathan, S. V. N.},
  volume = 	 {38},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {San Diego, California, USA},
  month = 	 {09--12 May},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v38/stoehr15.pdf},
  url = 	 {https://proceedings.mlr.press/v38/stoehr15.html},
  abstract = 	 {Gibbs random fields play an important role in statistics, however, the resulting likelihood is typically unavailable due to an intractable normalizing constant. Composite likelihoods offer 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.}
}