Abstract
Most documented Bayesian network (BN) applications have been built through knowledge elicitation from domain experts (DEs). The difficulties involved have led to growing interest in machine learning of BNs from data. There is a further need for combining what can be learned from the data with what can be elicited from DEs. In this paper, we propose a detailed methodology for this combination, specifically for the parameters of a BN.
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Woodberry, O., Nicholson, A.E., Korb, K.B., Pollino, C. (2004). Parameterising Bayesian Networks. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_108
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DOI: https://doi.org/10.1007/978-3-540-30549-1_108
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24059-4
Online ISBN: 978-3-540-30549-1
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