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Probabilistic Inference in Influence Diagrams
Author(s) -
Zhang Nevin Lianwen
Publication year - 1998
Publication title -
computational intelligence
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
ISBN - 1-55860-555-X
DOI - 10.1111/0824-7935.00073
Subject(s) - inference , computer science , frequentist inference , reduction (mathematics) , bayesian network , artificial intelligence , bayesian inference , probabilistic logic , influence diagram , machine learning , algorithm , bayesian probability , mathematics , decision tree , geometry
This paper is about reducing influence diagram (ID) evaluation into Bayesian network (BN) inference problems that are as easy to solve as possible. Such reduction is interesting because it enables one to readily use one's favorite BN inference algorithm to efficiently evaluate IDs. Two such reduction methods have been proposed previously (Cooper 1988; Shachter and Peot 1992). This paper proposes a new method. The BN inference problems induced by the new method are much easier to solve than those induced by the two previous methods.