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Author Heckerman, David ♦ Wellman, Michael P.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Abstract 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.”
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-08-01
Publisher Place New York
Journal Communications of the ACM (CACM)
Volume Number 38
Issue Number 3
Page Count 4
Starting Page 27
Ending Page 30

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Source: ACM Digital Library