Bayesian inference for mixtures of von Mises distributions using reversible jump MCMC sampler
Kees Mulder, Pieter Jongsma, Irene G. Klugkist · Journal of Statistical Computation and Simulation · 2020
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.