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.
