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.
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Pérez-Ariza, C.B., Nicholson, A.E., Korb, K.B., Mascaro, S., Hu, C.H. (2012). Causal Discovery of Dynamic Bayesian Networks. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_76
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DOI: https://doi.org/10.1007/978-3-642-35101-3_76
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