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