A Tutorial on Learning Bayesian Networks (1995)
| Venue: | Communications of the ACM |
| Citations: | 248 - 11 self |
BibTeX
@TECHREPORT{Heckerman95atutorial,
author = {David Heckerman},
title = {A Tutorial on Learning Bayesian Networks},
institution = {Communications of the ACM},
year = {1995}
}
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Abstract
We examine a graphical representation of uncertain knowledge called a Bayesian network. The representation is easy to construct and interpret, yet has formal probabilistic semantics making it suitable for statistical manipulation. We show how we can use the representation to learn new knowledge by combining domain knowledge with statistical data. 1 Introduction Many techniques for learning rely heavily on data. In contrast, the knowledge encoded in expert systems usually comes solely from an expert. In this paper, we examine a knowledge representation, called a Bayesian network, that lets us have the best of both worlds. Namely, the representation allows us to learn new knowledge by combining expert domain knowledge and statistical data. A Bayesian network is a graphical representation of uncertain knowledge that most people find easy to construct and interpret. In addition, the representation has formal probabilistic semantics, making it suitable for statistical manipulation (Howard,...
