CMU-CS-11-100
Lane Center for Computational Biology
School of Computer Science, Carnegie Mellon University


CMU-CB-11-100

The von Mises Graphical Model:
Structure Learning

Narges Sharif Razavian*, Hetunandan Kamisetty, Christopher James Langmead**

March 2011

CMU-CB-11-100.pdf

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.

20 pages


   *Language Technologies Institute, School of Computer Science
**Computer Science Department and Lane Center for Computational Biology, School of Computer Science

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