Causal discovery with prior information

Authors:
R. T. O'Donnell;A. E. Nicholson;B. Han;K. B. Korb;M. J. Alam;L. R. Hope
Affiliations:
Faculty of Information Technology, Monash University;Faculty of Information Technology, Monash University;Faculty of Information Technology, Monash University;Faculty of Information Technology, Monash University;Faculty of Information Technology, Monash University;Faculty of Information Technology, Monash University
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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Abstract

Bayesian networks (BNs) are rapidly becoming a leading tool in applied Artificial Intelligence (AI). BNs may be built by eliciting expert knowledge or learned via causal discovery programs. A hybrid approach is to incorporate prior information elicited from experts into the causal discovery process. We present several ways of using expert information as prior probabilities in the CaMML causal discovery program.