Distributed Parameter Estimation in Probabilistic Graphical Models
Machine Learning
2014-06-13 v1
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.
Cite
@article{arxiv.1406.3070,
title = {Distributed Parameter Estimation in Probabilistic Graphical Models},
author = {Yariv Dror Mizrahi and Misha Denil and Nando de Freitas},
journal= {arXiv preprint arXiv:1406.3070},
year = {2014}
}