Developing Complete Conditional Probability Tables from Fractional Data for Bayesian Belief Networks
Journal of Computing in Civil EngineeringPublished 16 June 2007Open access
Zhong Tang, Brenda McCabe
Citations53
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TL;DR
Techniques for using fractional data to develop complete conditional probability tables were examined and showed good predictability of the missing data in a linear domain by the piecewise representation method.
Abstract
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 k binary parent nodes is now 2k rather than 2k.
Keywords
Computer ScienceDecision Sciences
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