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