Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian networks applied to therapy monitoring
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
A Bayesian method for constructing Bayesian belief networks from databases
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
Operations for learning with graphical models
Journal of Artificial Intelligence Research
Knowledge-Based operations for graphical models in planning
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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In this paper, we present an algorithm for learning the most probable structure of a Bayesian Network from a database of cases. Starting from two previous algorithms, K2 of Cooper and Herskovits, and B of Buntine, we developed a new algorithm that relaxes the assumption of total ordering on the nodes needed by K2 and has less computations than B. To improve our algorithm, we added some heuristics and an interactive process with the user.