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Distributed Parameter Estimation in Probabilistic Graphical Models

Yariv D. Mizrahi, Misha Denil, Nando None de Freitas

Neural Information Processing Systems

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.


Distributed Parameter Estimation in Probabilistic Graphical Models University of British Columbia, Canada University of Oxford, United Kingdom

Neural Information Processing Systems

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.


Distributed Parameter Estimation in Probabilistic Graphical Models

Mizrahi, Yariv D., Denil, Misha, Freitas, Nando de

Neural Information Processing Systems

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.


Distributed Parameter Estimation in Probabilistic Graphical Models

Mizrahi, Yariv Dror, Denil, Misha, de Freitas, Nando

arXiv.org Machine Learning

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.