Learning Undirected Possibilistic Networks with Conditional Independence Tests

Abstract

Approaches based on conditional independence tests are among the most popular methods for learning graphical models from data. Due to the predominance of Bayesian networks in the field, they are usually developed for directed graphs. For possibilistic networks of a certain kind, however, undirected graphs are a more natural basis and thus algorithms for learning undirected graphs are desirable in this area. In this paper I present an algorithm for learning undirected graphical models, which is derived from the well-known Cheng-Bell-Liu algorithm. Its main advantage is the lower number of conditional independence tests that are needed, while it achieves results of comparable quality.