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.