CMU-CS-11-129
Computer Science Department
School of Computer Science, Carnegie Mellon University


CMU-CS-11-129

The von Mises Graphical Model:
Regularized Structure and Parameter Learning

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

September 2011

CMU-CS-11-129.pdf

Also appears as Lane Center for Computational Biology
Technical Report CMU-CB-11-101

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.

14 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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