Bayesian research / Priors
Priors for circular models
The concentration parameter controls how tightly a circular distribution gathers around its mean direction. A prior on that parameter expresses what we assume about directional variation before observing the data.
THE RESEARCH
Penalized complexity priors
Penalized complexity (PC) priors express a preference for a simpler base model and penalize departures from it. For von Mises concentration, two base models lead to different constructions: the circular uniform distribution and a point mass. Calibration through mean resultant length or angular spread makes the prior interpretable in terms of directional variation.
RELATED PAPERPenalizing complexity priors for Bayesian inference of circular models
Ye, Van Niekerk & Rue (2026)