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Convergent propagation algorithms via oriented trees

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by Amir Globerson
Venue:In UAI. 2007
Citations:8 - 3 self
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Metadata Version 1

DatumValueSource
TITLE Convergent propagation algorithms via oriented trees INFERENCE
AUTHOR NAME Amir Globerson SVM HeaderParse 0.2
AUTHOR AFFIL CSAIL; Massachusetts Institute of Technology SVM HeaderParse 0.2
AUTHOR ADDR Cambridge, MA 02139 SVM HeaderParse 0.2
ABSTRACT Inference problems in graphical models are often approximated by casting them as constrained optimization problems. Message passing algorithms, such as belief propagation, have previously been suggested as methods for solving these optimization problems. However, there are few convergence guarantees for such algorithms, and the algorithms are therefore not guaranteed to solve the corresponding optimization problem. Here we present an oriented tree decomposition algorithm that is guaranteed to converge to the global optimum of the Tree-Reweighted (TRW) variational problem. Our algorithm performs local updates in the convex dual of the TRW problem – an unconstrained generalized geometric program. Primal updates, also local, correspond to oriented reparametrization operations that leave the distribution intact. 1 SVM HeaderParse 0.2
VENUE In UAI. 2007 INFERENCE
VENUE TYPE CONFERENCE INFERENCE
CITATIONS 16 found ParsCit 1.0
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