BAYESIAN NETWORKS
BAYESIAN NETWORKS
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DOI：
10.1145/203330.203336
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发表时间：
1995-03-01
影响因子：
22.7
通讯作者：
WELLMAN, MP
中科院分区：
文献类型：
--
作者：
HECKERMAN, D;WELLMAN, MP
这篇关于贝叶斯网络的简短教程旨在向读者介绍本特殊部分中文章所使用的一些概念、术语和符号。在贝叶斯网络中，一个变量的值来自一组互斥的和集体穷举的状态。一个变量可以是离散的，具有有限或可数的状态数，也可以是连续的。通常，状态的选择本身就提出了一个有趣的建模问题。例如，在对打印问题进行故障排除的系统中，我们可以选择用两种状态（“存在”和“缺席”）对变量“打印输出”进行建模，或者我们可能希望用更细微的区别（如“缺席”、“模糊”、“切断”和“ok”）对变量进行建模。
This brief tutorial on Bayesian networks serves to introduce readers to some of the concepts, terminology, and notation employed by articles in this special section. In a Bayesian network, a variable takes on values from a collection of mutually exclusive and collective exhaustive states. A variable may be discrete, having a finite or countable number of states, or it may be continuous. Often the choice of states itself presents an interesting modeling question. For example, in a system for troubleshooting a problem with printing, we may choose to model the variable “print output” with two states—“present” and “absent”—or we may want to model the variable with finer distinctions such as “absent,” “blurred ,” “cut off,” and “ok.”