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Bayesian Model Choice for Directional Data

Fallaize, Christopher J.; Kypraios, Theodore

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Abstract

This article is concerned with the problem of choosing between competing models for directional data. In particular, we consider the question of whether or not two independent samples of axial data come from the same Bingham distribution. This is not a straightforward question to answer, due to the intractable nature of the parameter-dependent normalizing constant of the Bingham distribution. We propose three different methods to perform this task within a Bayesian framework, and apply the methodology to a real dataset on earthquakes in New Zealand. R code to run our methods is available in online supplementary materials.

Citation

Fallaize, C. J., & Kypraios, T. (2024). Bayesian Model Choice for Directional Data. Journal of Computational and Graphical Statistics, 33(1), 25-34. https://doi.org/10.1080/10618600.2023.2206076

Journal Article Type Article
Acceptance Date Apr 16, 2023
Online Publication Date Jun 13, 2023
Publication Date 2024
Deposit Date May 17, 2023
Publicly Available Date Mar 28, 2024
Journal Journal of Computational and Graphical Statistics
Print ISSN 1061-8600
Electronic ISSN 1537-2715
Publisher Taylor and Francis
Peer Reviewed Peer Reviewed
Volume 33
Issue 1
Pages 25-34
DOI https://doi.org/10.1080/10618600.2023.2206076
Keywords Bingham distribution; doubly intractable distributions; model choice; reversible jump MCMC
Public URL https://nottingham-repository.worktribe.com/output/19785102
Publisher URL https://www.tandfonline.com/doi/full/10.1080/10618600.2023.2206076

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