The von Mises Graphical Model: Structure Learning
Narges Sharif Razavian*, Hetunandan Kamisetty, Christopher James Langmead**
Also appears as Computer Science Department
Technical Report CMU-CS-11-108
Keywords: von Mises, Structure Learning, Generative Models, Probabilistic Graphical Models, LIRegularization, Time-Varying, Proteins, Molecular Dynamics
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 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.