安全临界系统决策不确定性的贝叶斯估计方法
A Bayesian Methodology for Estimating Uncertainty of Decisions in Safety-Critical Systems
摘要：
在安全关键工程应用中，决策的不确定性可以基于对决策模型进行平均的贝叶斯马尔可夫链蒙特卡罗（MCMC）技术来估计。使用决策树（DT）模型帮助专家解释因果关系，并找到不确定性的因素。贝叶斯平均也允许专家准确地估计不确定性时，先验信息的青睐结构的DT是可用的。然后，专家可以选择单个DT模型，通常是最大后验模型，用于解释目的。不幸的是，一个先验的信息，有利于结构的DT并不总是可用的。出于这个原因，我们提出了一个新的先验DT的贝叶斯MCMC技术。我们还提出了一个新的程序选择一个单一的DT和描述一个应用场景。在我们的短期冲突警报数据的实验中，我们的技术优于现有的贝叶斯技术在选定的单个DT的预测精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
