Date of Original Version



Technical Report

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All Rights Reserved

Abstract or Description

The von Mises model encodes a multivariate circular distribution as an undirected probabilistic graphical model. Presently, the only algorithm for performing inference in the model is Gibbs sampling, which becomes inefficient for large graphs. To address this issue, we introduce an Expectation Propagation based algorithm for performing inference in the von Mises graphical model. Our approach introduces a moment-matching technique for trigonometric functions to approximate the Expectation Propagation messages efficiently. We show that our algorithm has better speed of convergence and similar accuracy compared to Gibbs sampling, on synthetic data as well as real-world data from protein structures.