Causal Discovery of Dynamic Bayesian Networks

Cora Beatriz Pérez-Ariza¹, Ann E. Nicholson², Kevin B. Korb², Steven Mascaro², and Chao Heng Hu²

¹Dept. of Computer Science and Artificial Intelligence, University of Granada, Granada, 18071, Spain

²Clayton School of IT, Monash University, Clayton, VIC 3800, Australia

Abstract. While a great variety of algorithms have been developed and applied to learning static Bayesian networks, the learning of dynamic networks has been relatively neglected. The causal discovery program CaMML has been enhanced with a highly flexible set of methods for taking advantage of prior expert knowledge in the learning process. Here we describe how these representations of prior knowledge can be used instead to turn CaMML into a promising tool for learning dynamic Bayesian networks.

Keywords: Learning dynamic Bayesian networks, causal discovery, dynamic Bayesian networks

LNAI 7691, p. 902 ff.

Full article in PDF | BibTeX

lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012