Top-down versus bottom-up processing of influence diagrams in probabilistic analysis

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Recent work by Phillips et al., and Selby et al., has shown that influence diagram methodology can be a useful analytical tool in reactor safety studies. An influence diagram is a graphical representation of probabilistic dependence within a system or event sequence. Bayesian statistics are employed to transform the relationships depicted in the influence diagram into the correct expression for a desired marginal probability (e.g. the top event). As with fault trees, top-down and bottom-up algorithms have emerged as the dominant methods for quantifying influence diagrams. Purpose of this paper is to demonstrate a potential error in employing the bottom-up ... continued below

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Timmerman, R.D.; Burns, T.J. & Dodds, H.L. Jr. January 1, 1984.

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Recent work by Phillips et al., and Selby et al., has shown that influence diagram methodology can be a useful analytical tool in reactor safety studies. An influence diagram is a graphical representation of probabilistic dependence within a system or event sequence. Bayesian statistics are employed to transform the relationships depicted in the influence diagram into the correct expression for a desired marginal probability (e.g. the top event). As with fault trees, top-down and bottom-up algorithms have emerged as the dominant methods for quantifying influence diagrams. Purpose of this paper is to demonstrate a potential error in employing the bottom-up algorithm when dealing with interdependencies. In addition, the computing efficiency of both methods is discussed.

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Pages: 6

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NTIS, PC A02/MF A01.

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  • Joint meeting of the American Nuclear Society and the Atomic Industrial Forum, Washington, DC, USA, 11 Nov 1984

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  • Other: DE85005381
  • Report No.: CONF-841105-53
  • Grant Number: AC05-84OR21400
  • Office of Scientific & Technical Information Report Number: 6079284
  • Archival Resource Key: ark:/67531/metadc1113235

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  • January 1, 1984

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  • Feb. 22, 2018, 7:45 p.m.

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Timmerman, R.D.; Burns, T.J. & Dodds, H.L. Jr. Top-down versus bottom-up processing of influence diagrams in probabilistic analysis, article, January 1, 1984; United States. (digital.library.unt.edu/ark:/67531/metadc1113235/: accessed September 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.