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arXiv · 1302.1519

Update Rules for Parameter Estimation in Bayesian Networks

Eric Bauer·Daphne Koller·Yoram Singer
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Abstract

This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [Kivinen & Warmuth, 1994]. We provide a unified framework for parameter estimation that encompasses both on-line learning, where the model is continuously adapted to new data cases as they arrive, and the more traditional batch learning, where a pre-accumulated set of samples is used in a one-time model selection process. In the batch case, our framework encompasses both the gradient projection algorithm and the EM algorithm for Bayesian networks. The framework also leads to new on-line and batch parameter update schemes, including a parameterized version of EM. We provide both empirical and theoretical results indicating that parameterized EM allows faster convergence to the maximum likelihood parameters than does standard EM.

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Eric Bauer, Daphne Koller, Yoram Singer. 2013-02-06. Update Rules for Parameter Estimation in Bayesian Networks. https://arxiv.org/abs/1302.1519

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