A backward selection procedure for approximating a discrete probability distribution by decomposable models
Decomposable (probabilistic) models are log-linear models generated by acyclic hypergraphs, and a number of nice properties enjoyed by them are known. In many applications the following selection problem naturally arises: given a probability distribution over a finite set of discrete variables and a positive integer , find a decomposable model with tree-width that best fits . If is the generating hypergraph of a decomposable model and is the estimate of under the model, we can measure...