A Bayesian Method for Constructing Bayesian Belief Networks from Databases
By Gregory F. Cooper, Edward H. Herskovits. 1991
This paper presents a Bayesian method for constructing Bayesian belief networks from a database of cases. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. We relate the methods in this paper to previous work, and we discuss open problems.
Subfields: Probabilistic & Bayesian Methods
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In Probabilistic & Bayesian Methods
- Reza Babanezhad Harikandeh
- Rémi Le Priol
- Simon Lacoste-Julien
- Xabier Ugarte-Pedrero
- Igor Santos
- Kevin B. Korb
- Ann E. Nicholson
- Yoshua Bengio
Citations in the corpus
- An Analysis of the Adaptation Speed of Causal Models (2020, background): “This prior leads to the K2 score from the Bayesian network literature (Cooper and Herskovits, 1991).”
- Opcode sequences as representation of executables for data-mining-based unknown malware detection (2013, methodology): “– Bayesian networks (BN): We used several structural learning algorithms; K2 [18], Hill Climber [64] and Tree Augmented Näıve (TAN) [26].”
- Bayesian Artificial Intelligence (2004, background): “The first significant attempt at a Bayesian approach to learning discrete Bayesian networks without topological restrictions was made by Cooper and Herskovits in 1991 [53].”