The von Mises Graphical Model:
Regularized Structure and Parameter Learning
Narges Sharif Razavian*, Hetunandan Kamisetty, Christopher James Langmead**
Also appears as Computer Science Department
Technical Report CMU-CS-11-129
Keywords: Structure Learning, Regularization, von Mises, Probabilistic Graphical Models, Proteins
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.