Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Advances in probabilistic reasoning
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
A graph-based inference method for conditional independence
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
An algorithm for deciding if a set of observed independencies has a causal explanation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Constructor: a system for the induction of probabilistic models
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
A conditional independence algorithm for learning undirected graphical models
Journal of Computer and System Sciences
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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Previous algorithms for the construction of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required an ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches - CI tests are used to generate an ordering on the nodes from the database which is then used to recover the underlying Bayesian network structure using a non CI based method. Results of preliminary evaluation of the algorithm on two networks (ALARM and LED) are presented. We also discuss some algorithm performance issues and open problems.