Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
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arXiv
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| Format: | Preprint |
| Published: |
2015
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| _version_ | 1866910013515104256 |
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| author | Stoehr, Julien Friel, Nial |
| author_facet | Stoehr, Julien Friel, Nial |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1502_01997 |
| institution | arXiv |
| publishDate | 2015 |
| record_format | arxiv |
| spellingShingle | Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields Stoehr, Julien Friel, Nial Statistics Theory Computation 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. |
| title | Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields |
| topic | Statistics Theory Computation |
| url | https://arxiv.org/abs/1502.01997 |