Learning the Probability Distribution for Probabilistic Expert Systems
 Title:

Learning the Probability Distribution for Probabilistic Expert Systems
 Author(s):

Baker, Michelle
Roberts, Kenneth S.
 Date:

1989
 Type:

Technical reports
 Department:

Computer Science
 Persistent URL:

http://hdl.handle.net/10022/AC:P:12218
 Series:

Columbia University Computer Science Technical Reports
 Part Number:

CUCS49089
 Publisher:

Department of Computer Science, Columbia University
 Publisher Location:

New York
 Abstract:

This paper describes a method for learning the joint probability distribution of a set of variables from a sample of instances from the domain. The method is based on a straightforward application of Bayes Law to the problem of estimating individual probabilities from a probability distribution. We use a maximum entropy distribution as an initial estimate and show how this estimate can be easily updated each time an additional example is observed. Although developed for the purpose of estimating the conditional probabilities required for Bayesian inference networks, this method can be adopted to simplify knowledge acquisition in any expert system that uses knowledge in the form of probabilities.
 Subject(s):

Computer science
 Item views
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 Metadata:

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 Suggested Citation:

Michelle Baker, Kenneth S. Roberts,
1989,
Learning the Probability Distribution for Probabilistic Expert Systems, Columbia University Academic Commons,
http://hdl.handle.net/10022/AC:P:12218.