Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
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.
- Publication:
-
arXiv e-prints
- Pub Date:
- February 2015
- DOI:
- 10.48550/arXiv.1502.01997
- arXiv:
- arXiv:1502.01997
- Bibcode:
- 2015arXiv150201997S
- Keywords:
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- Mathematics - Statistics Theory;
- Statistics - Computation
- E-Print:
- JMLR Workshop and Conference Proceedings, 18th International Conference on Artificial Intelligence and Statistics (AISTATS), San Diego, California, USA, 9-12 May 2015 (Vol. 38, pp. 921-929). arXiv admin note: substantial text overlap with arXiv:1207.5758