Distributed Parameter Estimation in Probabilistic Graphical Models
  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. 
Yariv Dror Mizrahi and Misha Denil and Nando de Freitas
stat.ML