An Empirical Study of Probability Elicitation under Noisy-OR Assumption

May 2004

Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004)

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Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2004)

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Bayesian network is a popular modeling tool for uncertain domains that provides a compact representation of a joint probability distribution among a set of variables. Even though Bayesian networks significantly reduce the number of probabilities required to specify probabilistic relationships in the domain, the number of parameters required to quantify large models is still a serious bottleneck. Further reduction of parameters in a model is usually achieved by utilization of parametric probability distributions such as noisy-OR gates. In this paper report the results of an empirical study that suggests that under the assumption, that the underlying modeled distribution follows the noisy-OR assumptions, human experts provide parameters with better accuracy using elicitation of noisy-OR parameters than when eliciting conditional probability tables directly. It also seems that of the two alternative noisy-OR parameterizations due to Henrion and Diez the latter results in better elicitation accuracy.