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Learning the Probability Distribution for Probabilistic Expert Systems

Michelle Baker; Kenneth S. Roberts

Title:
Learning the Probability Distribution for Probabilistic Expert Systems
Author(s):
Baker, Michelle
Roberts, Kenneth S.
Date:
Type:
Technical reports
Department:
Computer Science
Permanent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-490-89
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:
161
Metadata:
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