Scalable Bayesian Computation for intractable likelihoods in image analysis

Moores, Matt, Alston, Clair, & Mengersen, Kerrie (2014) Scalable Bayesian Computation for intractable likelihoods in image analysis. In Fifth IMS-ISBA Joint Meeting (MCMSki IV), 6 - 8 January 2014, Chamonix, France. (Unpublished)


The inverse temperature hyperparameter of the hidden Potts model governs the strength of spatial cohesion and therefore has a substantial influence over the resulting model fit. The difficulty arises from the dependence of an intractable normalising constant on the value of the inverse temperature, thus there is no closed form solution for sampling from the distribution directly. We review three computational approaches for addressing this issue, namely pseudolikelihood, path sampling, and the approximate exchange algorithm. We compare the accuracy and scalability of these methods using a simulation study.

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ID Code: 87267
Item Type: Conference Item (Poster)
Refereed: No
Additional URLs:
Keywords: Hidden Markov random field, Image analysis, Intractable likelihood, Pseudo-marginal method, Markov chain Monte Carlo
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Divisions: Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 [please consult the authors]
Deposited On: 04 Sep 2015 00:25
Last Modified: 04 Sep 2015 00:25

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