In this paper we contribute two methods that simplify the demands of knowledge elicitation for particular types of Bayesian networks. The ?rst method simplify the task of providing probabilities when the states that a random variable takes can be described by a new, fully ordered state set in which a state implies all the preceding states. The second method leverages Dempster-Shafer theory of evidence to provide a way for the expert to express the degree of ignorance that they feel about the estimates being provided.
Revised: September 13, 2011 |
Published: April 16, 2011
Citation
Paulson P.R., T.E. Carroll, C. Sivaraman, P.A. Neorr, S.D. Unwin, and S.S. Hossain. 2011.Simplifying Probability Elicitation and Uncertainty Modeling in Bayesian Networks. In Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference (MAICS 2011), April 16-17, 2011, Cincinnati, OH, edited by S Visa, A Inoue and AL Ralescu, 114-119. Madison, Wisconsin:Omnipress.PNNL-SA-77781.