A bayesian method for the induction of probabilistic networks from data
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abstract: This paper presents a Bayesian method for constructing probabilistic networks from a database of cases. In particular, we focus on constructing Bayesian belief networks. Applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.
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| Abstract | This paper presents a Bayesian method for … This paper presents a Bayesian method for constructing probabilistic networks from a database of cases. In particular, we focus on constructing Bayesian belief networks. Applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems. evious work, and we discuss open problems. |
| Address | Stanford, CA, USA + |
| Author | Gregory F. Cooper +, and Edward Herskovits + |
| Bibtype | techreport + |
| Institution | Knowledge Systems, AI Laboratory + |
| Key | KSL-91-02 + |
| Month | January + |
| Number | KSL-91-02 + |
| Tag | Computer science + |
| Title | A Bayesian Method for the Induction of Probabilistic Networks from Data + |
| Tr id | KSL-91-02 + |
| Year | 1991 + |
