The von Mises Graphical Model: Regularized Structure and Parameter Learning (CMU-CS-11-129/CMU-CB-11-101)

The von Mises distribution is a continuous probability distribution on the circle used in directional statistics. In this paper, we introduce the undirected von Mises Graphical model and present an algorithm for parameter and structure learning using L₁ regularization. We show that the learning algorithm is both consistent and statistically efficient. Additionally, we introduce a simple inference algorithm based on Gibbs sampling. We compare the von Mises Graphical Model (VGM) with a Gaussian Graphical Model (GGM) on both synthetic data and on data from protein structures, and demonstrate that the VGM achieves higher accuracy than the GGM.