Factorized normalized maximum likelihood criterion for learning Bayesian network structures
Tomi Silander, Teemu Roos, Petri Kontkanen, Petri Myllymäki · 2008
This paper introduces a new scoring criterion, factorized normalized maximum likelihood, for learning Bayesian network structures. The proposed scoring criterion requires no parameter tuning, and it is decomposable and asymptotically consistent. We compare the new scoring criterion to other scoring criteria and describe its practical implementation. Empirical tests confirm its good performance.