an:05045442
Zbl 1096.68704
Jaeger, Manfred; Nielsen, Jens D.; Silander, Tomi
Learning probabilistic decision graphs
EN
Int. J. Approx. Reasoning 42, No. 1-2, 84-100 (2006).
00125485
2006
j
68T05
probabilistic models; learning
Summary: Probabilistic Decision Graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence relations that cannot be captured in a Bayesian network structure, and can sometimes provide computationally more efficient representations than Bayesian networks. In this paper we present an algorithm for learning PDGs from data. First experiments show that the algorithm is capable of learning optimal PDG representations in some cases, and that the computational efficiency of PDG models learned from real-life data is very close to the computational efficiency of Bayesian network models.