Approximating discrete probability distributions with decomposable models

A heuristic procedure is presented for approximating an n-dimensional discrete probability distribution with a decomposable model of a given complexity. It is shown that, without loss of generality, the search space can be restricted to a suitable subclass of decomposable models, whose members are called elementary models. The selected elementary model is constructed in an incremental manner according to a local-optimality criterion that consists of minimizing a suitable cost function. It is shown by an example that the solution computed by the procedure is sometimes optimal.>