The von Mises Graphical Model: Structure Learning (CMU-CS-11-108 / CMU-CB-11-100)

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 structure learning using L₁ regularization. We show that the learning algorithm is both consistent and efficient. We also introduce a simple inference algorithm based on Gibbs sampling. We compare and contrast 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.