Bayesian inference for mixtures of von Mises distributions using reversible jump MCMC sampler. Issue 9 (12th June 2020)

• Record Type: Journal Article
• Authors: Mulder, Kees; Jongsma, Pieter; Klugkist, Irene
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 9(2020)
• Page Start: 1539
• Page End: 1556
• Publication Date: 2020-06-12
• Subjects: Markov chain Monte Carlo -- circular statistics -- von Mises -- mixture models; 62F15; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1740997
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : Circular data are encountered in a variety of fields. A dataset on music listening behaviour throughout the day motivates development of models for multi-modal circular data where the number of modes is not known a priori. To fit a mixture model with an unknown number of modes, the reversible jump Metropolis-Hastings MCMC algorithm is adapted for circular data and presented. The performance of this sampler is investigated in a simulation study. At small-to-medium sample sizes ( n ≤ 100 ), the number of components is uncertain. At larger sample sizes ( n ≥ 500 ) the estimation of the number of components is accurate. Application to the music listening data shows interpretable results that correspond with intuition.