Learning probabilistic decision graphs

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Learning probabilistic decision graphs
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.
Manfred Jaeger, Jens D. Nielsen, Tomi Silander
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2006
Where IJAR
Authors Manfred Jaeger, Jens D. Nielsen, Tomi Silander
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