Abstract
In this chapter we will address how graphical models can be learned from given data. So far we were given the graphical structure. Now, we will introduce heuristics that allow us to induce these structures.
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References
G.F. Cooper and E. Herskovits. A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning 9:309–347. Kluwer, Dordrecht, Netherlands, 1992
D. Heckerman, D. Geiger, and D.M. Chickering. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data, MSR-TR-94-09. Microsoft Research, Advanced Technology Division, Redmond, WA, USA, 1994
R.W. Robinson. Counting Unlabeled Acyclic Digraphs. In: C.H.C. Little (ed.) Combinatorial Mathematics V. LNMA 622:28–43. Springer-Verlag, Heidelberg, Germany, 1977
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Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P. (2013). Learning Graphical Models. In: Computational Intelligence. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-5013-8_25
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DOI: https://doi.org/10.1007/978-1-4471-5013-8_25
Publisher Name: Springer, London
Print ISBN: 978-1-4471-5012-1
Online ISBN: 978-1-4471-5013-8
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