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Distributed Parameter Estimation in Probabilistic Graphical Models

Yariv Mizrahi‚ Misha Denil and Nando de Freitas

Abstract

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.

Book Title
Advances in Neural Information Processing Systems (NIPS)
Institution
University of Oxford
Year
2014