LEARNING BAYESIAN NETWORKS WITH LARGEST CHAIN GRAPHS

摘要

This paper proposes a new approach for designing learning bayesian network algorithms that explore the structure equivalence classes space. Its main originality consists in the representation of equivalence classes by largest chain graphs, instead of essential graphs which are generally used in the similar task. We show that this approach drastically simplifies the algorithms formulation and has some beneficial aspects on their execution time.