Distributed Parameter Estimation in Probabilistic Graphical Models

Part of Advances in Neural Information Processing Systems 27 (NIPS 2014)

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Yariv D. Mizrahi, Misha Denil, Nando de Freitas


This paper presents foundational theoretical results on distributed parameter estimation for undirected probabilistic graphical models. It introduces a general condition on composite likelihood decompositions of these models which guarantees the global consistency of distributed estimators, provided the local estimators are consistent.