Building Bayesian belief networks in the absence of data involves the challenging task of eliciting conditional probabilities from experts. In this paper, we develop analytical methods for determining the order in which parameters are to be elicited, based on a proximity criteria for the distribution of either the entire set of variables, or a subset of variables of primary interest to the analyst. We explore the implications of our results for typical parameter prior distributions used in the learning community, such as the uniform Dirichlet distribution. Through experiments, we compare the influence of the chosen variables of interest on the ordering.
By: Debarun Bhattacharjya; Lea A. Deleris; Bonnie Ray
Published in: RC24818 in 2009
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