Exact Bayesian inference for the Bingham distribution
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,...
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| Format: | Article |
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Springer
2016
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| Online Access: | https://eprints.nottingham.ac.uk/35016/ |
| _version_ | 1848794984045608960 |
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| author | Fallaize, Christopher J. Kypraios, Theodore |
| author_facet | Fallaize, Christopher J. Kypraios, Theodore |
| author_sort | Fallaize, Christopher J. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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 |
| first_indexed | 2025-11-14T19:24:52Z |
| format | Article |
| id | nottingham-35016 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:24:52Z |
| publishDate | 2016 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-350162020-05-04T20:04:32Z https://eprints.nottingham.ac.uk/35016/ Exact Bayesian inference for the Bingham distribution Fallaize, Christopher J. Kypraios, Theodore 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 Springer 2016-01 Article PeerReviewed Fallaize, Christopher J. and Kypraios, Theodore (2016) Exact Bayesian inference for the Bingham distribution. Statistics and Computing, 26 (1). pp. 349-360. ISSN 1573-1375 Directional statistics; Bayesian inference; Markov Chain Monte Carlo; Doubly intractable distributions http://link.springer.com/article/10.1007/s11222-014-9508-7 doi:10.1007/s11222-014-9508-7 doi:10.1007/s11222-014-9508-7 |
| spellingShingle | Directional statistics; Bayesian inference; Markov Chain Monte Carlo; Doubly intractable distributions Fallaize, Christopher J. Kypraios, Theodore Exact Bayesian inference for the Bingham distribution |
| title | Exact Bayesian inference for the Bingham distribution |
| title_full | Exact Bayesian inference for the Bingham distribution |
| title_fullStr | Exact Bayesian inference for the Bingham distribution |
| title_full_unstemmed | Exact Bayesian inference for the Bingham distribution |
| title_short | Exact Bayesian inference for the Bingham distribution |
| title_sort | exact bayesian inference for the bingham distribution |
| topic | Directional statistics; Bayesian inference; Markov Chain Monte Carlo; Doubly intractable distributions |
| url | https://eprints.nottingham.ac.uk/35016/ https://eprints.nottingham.ac.uk/35016/ https://eprints.nottingham.ac.uk/35016/ |