Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICASSP.2006.1661375 | 2006 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-13 |
Keywords | Field | DocType |
bayesian methods,factor graph,factor graphs,maximum likelihood estimate,bayesian statistics,gaussian processes,optimization problem,belief propagation,computational complexity,maximum likelihood estimation,iterative methods,critical point,gauss seidel | Factor graph,Mathematical optimization,Pattern recognition,Iterative method,Stationary point,Artificial intelligence,Statistical inference,Expectation propagation,Optimization problem,Gauss–Seidel method,Mathematics,Belief propagation | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John MacLaren Walsh | 1 | 107 | 17.90 |
| Phillip A. Regalia | 2 | 377 | 106.45 |