Probabilistic Inference in Influence Diagrams

Nevin Lianwen Zhang
1998 Computational intelligence  
This paper studies the relationship between probabilistic inference in Bayesian networks and evaluation of in uence diagrams. We clearly identify and separate from other computations probabilistic inference subproblems that must be solved in order to evaluate an in uence diagram. This work leads to a new method for evaluating inuence diagrams where arbitrary Bayesian network inference algorithms can be used for probabilistic inference. We argue that if the VE algorithm Poole 1994, Zhang and
more » ... e 1996) is used for probabilistic inference, the new method is more e cient than the best previous methods (Shenoy 1992 , Jensen et al 1994 . If a more e cient probabilistic inference algorithm such as the VEC algorithm (Zhang and Poole 1996) or some approximation algorithm is used, the new method can be even more e cient.
doi:10.1111/0824-7935.00073 fatcat:wsqesrrbrfeerg2aip36btopme