Conference proceeding
Iterative constrained maximum likelihood estimation via expectation propagation
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, v 5, pp V713-V716
2006
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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Details
- Title
- Iterative constrained maximum likelihood estimation via expectation propagation
- Creators
- John Mac Laren Walsh - Cornell UniversityPhillip A. Regalia - Catholic University of America
- Publication Details
- 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, v 5, pp V713-V716
- Conference
- IEEE International Conference on Acoustics Speech and Signal Processing (Toulouse, France, 14 May 2006–19 May 2006)
- Number of pages
- 4
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Scopus ID
- 2-s2.0-33947625659
- Other Identifier
- 991022096382404721