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arXiv · 1210.4910

New Advances and Theoretical Insights into EDML

Khaled S. Refaat·Arthur Choi·Adnan Darwiche
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Abstract

EDML is a recently proposed algorithm for learning MAP parameters in Bayesian networks. In this paper, we present a number of new advances and insights on the EDML algorithm. First, we provide the multivalued extension of EDML, originally proposed for Bayesian networks over binary variables. Next, we identify a simplified characterization of EDML that further implies a simple fixed-point algorithm for the convex optimization problem that underlies it. This characterization further reveals a connection between EDML and EM: a fixed point of EDML is a fixed point of EM, and vice versa. We thus identify also a new characterization of EM fixed points, but in the semantics of EDML. Finally, we propose a hybrid EDML/EM algorithm that takes advantage of the improved empirical convergence behavior of EDML, while maintaining the monotonic improvement property of EM.

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Khaled S. Refaat, Arthur Choi, Adnan Darwiche. 2012-10-16. New Advances and Theoretical Insights into EDML. https://arxiv.org/abs/1210.4910

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