Bayesian Model Choice for Directional Data

Fallaize, Christopher J.; Kypraios, Theodore

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: Jun 14, 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