Abstract
In this book we consider the use of Bayesian networks to model the underlying process. The process of inducing a Bayesian network from a database of cases and expert knowledge consists of two main steps. The first step is to induce the structure of the model, that is, the DAG, while the second step is to estimate the parameters of the model as defined by the structure. In this chapter we consider only discrete Bayesian networks. Thus, the task of data-driven modeling is to construct a Bayesian network\(\mathcal{N} = (\mathcal{X},\mathcal{G},\mathcal{P})\) from the available information sources. In general, the problem of inducing the structure of a Bayesian network is NP-complete (Chickering 1996). Thus, heuristic methods are appropriate.
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© 2013 Springer Science+Business Media New York
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Kjærulff, U.B., Madsen, A.L. (2013). Data-Driven Modeling. In: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. Information Science and Statistics, vol 22. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5104-4_8
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DOI: https://doi.org/10.1007/978-1-4614-5104-4_8
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-5103-7
Online ISBN: 978-1-4614-5104-4
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