Learning belief networks from data
- 1 January 1997
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 325-331
- https://doi.org/10.1145/266714.266920
Abstract
This paper presents an efficient algorithm for learning Bayesianbelief networks from databases. The algorithm takes a databaseas input and constructs the belief network structure as output.The construction process is based on the computation of mutualinformation of attribute pairs. Given a data set that is largeenough, this algorithm can generate a belief network very closeto the underlying model, and at the same time, enjoys the timecomplexity of O N ( )4on conditional independence...Keywords
This publication has 7 references indexed in Scilit:
- A guide to the literature on learning probabilistic networks from dataIEEE Transactions on Knowledge and Data Engineering, 1996
- Construction of Bayesian network structures from data: A brief survey and an efficient algorithmInternational Journal of Approximate Reasoning, 1995
- A Bayesian Method for the Induction of Probabilistic Networks from DataMachine Learning, 1992
- An Algorithm for Fast Recovery of Sparse Causal GraphsSocial Science Computer Review, 1991
- The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief NetworksPublished by Springer Nature ,1989
- Graphical and Recursive Models for Contingency TablesBiometrika, 1983
- Approximating discrete probability distributions with dependence treesIEEE Transactions on Information Theory, 1968