Abstract
We will now address the third question from Chap. 21, namely how graphical models can be learned from given data. Until now, we were given the graphical structure. Now, we will introduce heuristics that allow us to induce these structures.
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References
E. Castillo, J.M. Gutiérrez, A.S. Hadi, Expert Systems and Probabilistic Network Models (Springer, New York, 1997)
G.F. Cooper, E. Herskovits, A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309–347 (1992). Kluwer, Dordrecht, Netherlands
D. Heckerman, D. Geiger, 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 Combinatorial Mathematics V LNMA 622:28–43, ed. by C.H.C. Little (Springer, Heidelberg, 1977)
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Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., Steinbrecher, M. (2016). Learning Graphical Models. In: Computational Intelligence. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-7296-3_25
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DOI: https://doi.org/10.1007/978-1-4471-7296-3_25
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