Approximate Expectation Maximization

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Tom Heskes, Onno Zoeter, Wim Wiegerinck


We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood learning of Bayesian networks with belief propagation algorithms for approximate inference. Specifically we propose to combine the outer-loop step of convergent belief propagation algorithms with the M-step of the EM algorithm. This then yields an approximate EM algorithm that is essentially still double loop, with the important advantage of an inner loop that is guaranteed to converge. Simulations illustrate the merits of such an approach.