Bayesian networks provide a general framework with which to model many natural phenomena. The mathematical nature of Bayesian networks enables a plethora of model validation and calibration techniques: e.g parameter estimation, goodness of fit tests, and diagnostic checking of the model assumptions. However, they are not free of shortcomings. Parameter estimation from relevant extant data is a common approach to calibrating the model parameters. In practice it is not uncommon to find oneself lacking adequate data to reliably estimate all model parameters. In this paper we present the early development of a novel application of conjoint analysis as a method for eliciting and modeling expert opinions and using the results in a methodology for calibrating the parameters of a Bayesian network.
Revised: August 18, 2010 |
Published: May 23, 2010
Citation
Walsh S.J., A.C. Dalton, P.D. Whitney, and A.M. White. 2010.Parameterizing Bayesian network Representations of Social-Behavioral Models by Expert Elicitation. In IEEE International Conference on Intelligence and Security Informatics (ISI 2010), 227-232. Piscataway, New Jersey:Institute of Electrical and Electronics Engineers, Inc,.PNNL-SA-72080.doi:10.1109/ISI.2010.5484730