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Bayesian model comparison with un-normalised likelihood

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Everitt, R. G., Johansen, Adam M., Rowing, E. and Evdemon-Hogan, M. (2017) Bayesian model comparison with un-normalised likelihood. Statistics and Computing, 27 (2). pp. 403-422. doi:10.1007/s11222-016-9629-2 ISSN 0960-3174.

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Official URL: http://dx.doi.org/10.1007/s11222-016-9629-2

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Abstract

Models for which the likelihood function can be evaluated only up to a parameter-dependent unknown normalizing constant, such as Markov random field models, are used widely in computer science, statistical physics, spatial statistics, and network analysis. However, Bayesian analysis of these models using standard Monte Carlo methods is not possible due to the intractability of their likelihood functions. Several methods that permit exact, or close to exact, simulation from the posterior distribution have recently been developed. However, estimating the evidence and Bayes’ factors for these models remains challenging in general. This paper describes new random weight importance sampling and sequential Monte Carlo methods for estimating BFs that use simulation to circumvent the evaluation of the intractable likelihood, and compares them to existing methods. In some cases we observe an advantage in the use of biased weight estimates. An initial investigation into the theoretical and empirical properties of this class of methods is presented. Some support for the use of biased estimates is presented, but we advocate caution in the use of such estimates.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Q Science > QC Physics
Q Science > QP Physiology
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH): Bayesian statistical decision theory, Sampling (Statistics), Markov random fields , Movement sequences, Monte Carlo method
Journal or Publication Title: Statistics and Computing
Publisher: Springer
ISSN: 0960-3174
Official Date: March 2017
Dates:
DateEvent
March 2017Published
8 February 2016Available
20 January 2016Accepted
Volume: 27
Number: 2
Number of Pages: 20
Page Range: pp. 403-422
DOI: 10.1007/s11222-016-9629-2
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 29 January 2016
Date of first compliant Open Access: 8 February 2017

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