Edwards W
University of California, Los Angeles, USA.
Am Psychol. 1998 Apr;53(4):416-28. doi: 10.1037//0003-066x.53.4.416.
Bayes Nets (BNs) and Influence Diagrams (IDs), new tools that use graphic user interfaces to facilitate representation of complex inference and decision structures, will be the core elements of new computer technologies that will make the 21st century the Century of Bayes. BNs are a way of representing a set of related uncertainties. They facilitate Bayesian inference by separating structural information from parameters. Hailfinder is a BN that predicts severe summer weather in Eastern Colorado. Its design led to a number of novel ideas about how to build such BNs. Issues addressed included representation of spatial location, categorization of days, system boundaries, pruning, and methods for eliciting and checking on the appropriateness of conditional probabilities. The technology of BNs is improving rapidly. Especially important is the emergence of ways of reusing fragments of BNs. BNs and IDs are not just important design tools; they also represent a major enhancement of the understanding about how important intellectual tasks typically performed by people should and can be performed.
贝叶斯网络（BNs）和影响图（IDs）是使用图形用户界面来促进复杂推理和决策结构表示的新工具，它们将成为新计算机技术的核心要素，这些技术将使21世纪成为贝叶斯世纪。贝叶斯网络是一种表示一组相关不确定性的方式。它们通过将结构信息与参数分离来促进贝叶斯推理。Hailfinder是一个预测科罗拉多州东部夏季恶劣天气的贝叶斯网络。它的设计引发了许多关于如何构建此类贝叶斯网络的新颖想法。所涉及的问题包括空间位置的表示、日期分类、系统边界、剪枝以及引出和检查条件概率适用性的方法。贝叶斯网络技术正在迅速发展。特别重要的是出现了重用贝叶斯网络片段的方法。贝叶斯网络和影响图不仅是重要的设计工具；它们还极大地增强了人们对通常由人执行的重要智力任务应该如何以及能够如何执行的理解。
