Top-down versus bottom-up processing of influence diagrams in probabilistic analysis
Recent work by Phillips and Selby has shown that influence diagram methodology can be a useful analytical tool in reactor safety studies. In some instances an influence diagram can be used as a graphical representation of probabilistic dependence within a system or event sequence. Under these circumstances, Bayesian statistics is employed to transform the relationships depicted in the influence diagram into the correct expression for a desired marginal probability (e.g. the top node). Top-down and bottom-up algorithms have emerged as the dominant methods for quantifying influence diagrams. The purpose of this paper is to demonstrate a potential error in employing the bottom-up algorithm when dealing with interdependencies.
- Research Organization:
- Oak Ridge National Lab., TN (USA); Tennessee Univ., Knoxville (USA)
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 5554965
- Report Number(s):
- CONF-860610-2; ON: TI86005151
- Resource Relation:
- Conference: American Nuclear Society annual meeting, Reno, NV, USA, 15 Jun 1986
- Country of Publication:
- United States
- Language:
- English
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