Abstract:
This paper presents Bayesian directional data modeling via the
skew-rotationally-symmetric Fisher-von Mises-Langevin (FvML) distribution. The
prior distributions for the parameters are a pivotal building block in Bayesian analysis,
therefore, the impact of the proposed priors will be quantified using the Wasserstein
Impact Measure (WIM) to guide the practitioner in the implementation process. For the
computation of the posterior, modifications of Gibbs and slice samplings are applied for
generating samples. We demonstrate the applicability of our contribution via synthetic
and real data analyses. Our investigation paves the way for Bayesian analysis of skew
circular and spherical data.
