Distributed Parameter Estimation in Probabilistic Graphical Models

This provides foundational theoretical guarantees for distributed learning in probabilistic models, which is incremental but important for scalable machine learning applications.

The paper tackles the problem of distributed parameter estimation in undirected probabilistic graphical models by introducing a general condition on composite likelihood decompositions that ensures global consistency when local estimators are consistent.