Model similarity and robustness in predictions from Bayesian networks

Authors:
Sung-Ho Kim;Geon Youp Noh
Affiliations:
Korea Advanced Institute of Science and Technology, Daejeon, South Korea;Korea Advanced Institute of Science and Technology, Daejeon, South Korea
Venue:
MIC '07 Proceedings of the 26th IASTED International Conference on Modelling, Identification, and Control
Year:
2007

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Abstract

Consider a prediction system based on a Bayesian network (BN) where all the variables involved are binary, each taking on 0 or 1. The system categorizes the probability that a certain variable is equal to 1 conditional on a set of variables in an ascending order of the probability values and predicts for the variable in terms of category levels. We introduce a similarity measure between BN models and describe how a BN model can be constructed which is similar to a given BN model. Then under the condition that all the variables are positively associated with each other, a method of obtaining an agreement level of predictions between two BN models is proposed. The agreement levels are obtained by a simulation experiment for a simple BN model.