Distributed Parameter Estimation in Probabilistic Graphical Models
Yariv Mizrahi‚ Misha Denil and 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.
Advances in Neural Information Processing Systems (NIPS)
University of Oxford