| Paper: | MLSP-P2.7 | ||
| Session: | Learning Theory and Modeling | ||
| Time: | Tuesday, May 16, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Bayesian Learning and Modeling | ||
| Title: | Iterative Constrained Maximum Likelihood Estimation via Expectation Propagation | ||
| Authors: | John Walsh, Cornell University, United States; Phillip Regalia, Catholic University of America, United States | ||
| Abstract: | Expectation propagation defines a family of algorithms for approximate Bayesian statistical inference which generalize belief propagation on factor graphs with loops. As is the case for belief propagation in loopy factor graphs, it is not well understood why the stationary points of expectation propagation can yield good estimates. In this paper, given a reciprocity condition which holds in most cases, we provide a constrained maximum likelihood estimation problem whose critical points yield the stationary points of expectation propagation. Expectation propagation may then be interpreted as a nonlinear block Gauss Seidel method seeking a critical point of this optimization problem. | ||
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