By David Heckerman, Michael P. Wellman
Communications of the ACM,
March 1995,
Vol. 38 No. 3, Pages 27-30
10.1145/203330.203336
Comments
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.”
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