A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems

Details A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems

2010-12-01

arxiv.org/abs/1012.0322v1

Frontiers in Artificial Intelligence and Applications. Volume 149, IOS Press Book, 2006. Integrated Intelligent Systems for En

Computer Science

Artificial Intelligence

Scientific paper

Uncertainty of decisions in safety-critical engineering applications can be estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC) technique of averaging over decision models. The use of decision tree (DT) models assists experts to interpret causal relations and find factors of the uncertainty. Bayesian averaging also allows experts to estimate the uncertainty accurately when a priori information on the favored structure of DTs is available. Then an expert can select a single DT model, typically the Maximum a Posteriori model, for interpretation purposes. Unfortunately, a priori information on favored structure of DTs is not always available. For this reason, we suggest a new prior on DTs for the Bayesian MCMC technique. We also suggest a new procedure of selecting a single DT and describe an application scenario. In our experiments on the Short-Term Conflict Alert data our technique outperforms the existing Bayesian techniques in predictive accuracy of the selected single DTs.

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