CMU-CS-11-108
Computer Science Department
School of Computer Science, Carnegie Mellon University


CMU-CS-11-108

The von Mises Graphical Model:
Structure Learning

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

March 2011

CMU-CS-11-108.pdf


Keywords: Von Mises, Structure Learning, Generative Models, Probabilistic Graphic Models, LI Regularization, 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 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.

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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