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||Authors:||Friel, Nial
|Permanent link:||http://hdl.handle.net/10197/6514||Date:||12-May-2015||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.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||Microtome Publishing||Copyright (published version):||2015 the authors||Keywords:||Machine learning; Statistics||Language:||en||Status of Item:||Peer reviewed||Is part of:||18th International Conference on Artificial Intelligence and Statistics (AISTATS), San Diego, California, USA, 9-12 May 2015|
|Appears in Collections:||Mathematics and Statistics Research Collection|
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