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

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Title: Calibration of conditional composite likelihood for Bayesian inference on Gibbs random fields
Authors: Friel, Nial
Stoer, Julien
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 learningStatistics
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
Insight Research Collection

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