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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Lifted belief propagation: Pairwise marginals and beyond
 Myllymäki, P.; Roos, T.; Jaakkola, T. ; Helsinki Institute for Information Technology HIIT: Proceedings of the 5th European Workshop on Probabilistic Graphical Models, PGM 2010 2010 ISBN: 9789526033143 S.916 
 European Workshop on Probabilistic Graphical Models (PGM) <5, 2010, Helsinki> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer IAIS () 
Abstract
Lifted belief propagation (LBP) can be extremely fast at computing approximate marginal probability distributions over single variables and neighboring ones in the underlying graphical model. It does, however, not prescribe a way to compute joint distributions over pairs, triples or ktuples of distant random variables. In this paper, we present an algorithm, called conditioned LBP, for approximating these distributions. Essentially, we select variables one at a time for conditioning, running lifted belief propagation after each selection. This naive solution, however, recomputes the lifted network in each step from scratch, therefore often canceling the benefits of lifted inference. We show how to avoid this by efficiently computing the lifted network for each conditioning directly from the one already known for the single node marginals. Our experimental results validate that significant efficiency gains are possible and illustrate the potential for secondorder param eter estimation of Markov logic networks.