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Parameter Learning in Object-Oriented Bayesian Networks

Research paper by Helge Langseth, Olav Bangsø

Indexed on: 01 Aug '01Published on: 01 Aug '01Published in: Annals of Mathematics and Artificial Intelligence



Abstract

This paper describes a method for parameter learning in Object-Oriented Bayesian Networks (OOBNs). We propose a methodology for learning parameters in OOBNs, and prove that maintaining the object orientation imposed by the prior model will increase the learning speed in object-oriented domains. We also propose a method to efficiently estimate the probability parameters in domains that are not strictly object oriented. Finally, we attack type uncertainty, a special case of model uncertainty typical to object-oriented domains.