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contributor authorZhong Tang
contributor authorBrenda McCabe
date accessioned2017-05-08T21:13:21Z
date available2017-05-08T21:13:21Z
date copyrightJuly 2007
date issued2007
identifier other%28asce%290887-3801%282007%2921%3A4%28265%29.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/43326
description abstractA Bayesian belief network (BBN) can be a powerful tool in decision making processes. Development of a BBN requires data or expert knowledge to assist in determining the structure and probabilistic parameters in the model. As data are seldom available in the engineering decision making domain, a major barrier in using domain experts is that they are often required to supply a huge and intractable number of probabilities. Techniques for using fractional data to develop complete conditional probability tables were examined. The results showed good predictability of the missing data in a linear domain by the piecewise representation method. By using piecewise representation, the number of probabilities to be elicited for a binary child node with
publisherAmerican Society of Civil Engineers
titleDeveloping Complete Conditional Probability Tables from Fractional Data for Bayesian Belief Networks
typeJournal Paper
journal volume21
journal issue4
journal titleJournal of Computing in Civil Engineering
identifier doi10.1061/(ASCE)0887-3801(2007)21:4(265)
treeJournal of Computing in Civil Engineering:;2007:;Volume ( 021 ):;issue: 004
contenttypeFulltext


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