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.
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O’Donnell, R.T., Nicholson, A.E., Han, B., Korb, K.B., Alam, M.J., Hope, L.R. (2006). Causal Discovery with Prior Information. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_141
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DOI: https://doi.org/10.1007/11941439_141
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
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