Distributed Parameter Estimation in Probabilistic Graphical Models

2014/06/11 by Yariv Dror Mizrahi, Misha Denil, Mizrahi, Yariv Dror +3 · 1 citation
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Statistical Methods and Inference #stat.ML

paper · pdf · doi:10.48550/arxiv.1406.3070

arxiv created 2014/06/11 · openalex publication_date 2014/06/11 · arxiv updated 2014/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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• Structure learning of undirected graphical models for count data

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