A flexible Bayesian mixture approach for multi-modal circular data

In this article, we consider multi-modal circular data and nonparametric inference. We introduce a doubly flexible method based on Dirichlet process mixtures in which parameter assumptions are relaxed. assess discuss simulation studies the efficiency of proposed extension relative to standard finite mixture applications analysis data. The real application shows that relaxed approach is promising for making important contributions our understanding many real-life phenomena particularly environmental sciences such as animal orientations.