Scalable methods for learning Bayesian networks

Details Scalable methods for learning Bayesian networks Scalable methods for learning Bayesian networks

2007-07-31

2007-07-31

Knight, Anthony (Department: 2121)

Data processing: artificial intelligence

Machine learning

Reexamination Certificate

active

10732074

ABSTRACT:
The present invention leverages scalable learning methods to efficiently obtain a Bayesian network for a set of variables of which the total ordering in a domain is known. Certain criteria are employed to generate a Bayesian network which is then evaluated and utilized as a guide to generate another Bayesian network for the set of variables. Successive iterations are performed utilizing a prior Bayesian network as a guide until a stopping criterion is reached, yielding a best-effort Bayesian network for the set of variables.

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