Haddawy P, Helwig J W, Ngo L, Krieger R A
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee 53201, USA.
Proc Annu Symp Comput Appl Med Care. 1995:203-7.
We present a language for representing context-sensitive temporal probabilistic knowledge. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language and an implemented algorithm (BNG) that generates Bayesian networks to compute the posterior probabilities of queries. We illustrate the use of the BNG system by applying it to the problem of modeling the effects of medications and other interventions on the condition of a patient in cardiac arrest.
我们提出了一种用于表示上下文相关的时态概率知识的语言。上下文约束使得推理能够仅聚焦于概率知识的相关部分。我们为我们的语言提供了一种声明性语义，并提供了一种已实现的算法（BNG），该算法生成贝叶斯网络以计算查询的后验概率。我们通过将BNG系统应用于对药物和其他干预措施对心脏骤停患者状况的影响进行建模的问题，来说明该系统的使用。
