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Exact Bayesian inference for the Bingham distribution

Fallaize, Christopher J.; Kypraios, Theodore

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This paper is concerned with making Bayesian inference from data that are assumed to be drawn from a Bingham distribution. A barrier to the Bayesian approach is the parameter-dependent normalising constant of the Bingham distribution, which, even when it can be evaluated or accurately approximated, would have to be calculated at each iteration of an MCMC scheme, thereby greatly increasing the computational burden. We propose a method which enables exact (in Monte Carlo sense) Bayesian inference for the unknown parameters of the Bingham distribution by completely avoiding the need to evaluate this constant. We apply the method to simulated and real data, and illustrate that it is simpler to implement, faster, and performs better than an alternative algorithm that has recently been proposed in the literature


Fallaize, C. J., & Kypraios, T. (in press). Exact Bayesian inference for the Bingham distribution. Statistics and Computing, 26(1),

Journal Article Type Article
Acceptance Date Aug 20, 2014
Online Publication Date Jan 1, 2016
Deposit Date Jul 14, 2016
Publicly Available Date Jul 14, 2016
Journal Statistics and Computing
Print ISSN 0960-3174
Electronic ISSN 1573-1375
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 26
Issue 1
Keywords Directional statistics; Bayesian inference; Markov Chain Monte Carlo; Doubly intractable distributions
Public URL
Publisher URL
Additional Information The final publication is available at Springer via
Contract Date Jul 14, 2016


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