A Study of Approximate Inference in Probabilistic Relational Models

Fabian Kaelin (McGill) and Doina Precup (McGill); JMLR W&P 13:315-330, 2010.

Abstract

We tackle the problem of approximate inference in Probabilistic Relational Models (PRMs) and propose the Lazy Aggregation Block Gibbs (LABG) algorithm. The LABG algorithm makes use of the inherent relational structure of the ground Bayesian network corresponding to a PRM. We evaluate our approach on artificial and real data and show that it scales well with the size of the data set.



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