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Bayesian network models with discrete and continuous variables. (English) Zbl 1117.90008

Lucas, Peter (ed.) et al., Advances in probabilistic graphical models. Selected papers based on the presentations at the 2nd European workshop on probabilistic graphical models (PGM 2004), Leiden, The Netherlands, October 4–8, 2004. Berlin: Springer (ISBN 978-3-540-68994-2/bhk). Studies in Fuzziness and Soft Computing 213, 81-102 (2007).
Summary: Bayesian networks are powerful tools for handling problems which are specified through a multivariate probability distribution. A broad background of theory and methods has been developed for the case in which all the variables are discrete. However, situations in which continuous and discrete variables coexist in the same problem are common in practice. In such cases, usually the continuous variables are discretized and therefore all the existing methods for discrete variables can be applied, but the price to pay is that the obtained model is just an approximation. In this chapter we study two frameworks where continuous and discrete variables can be handled simultaneously without using discretization. These models are based on the CG and MTE distributions.
For the entire collection see [Zbl 1108.90003].

MSC:

90B10 Deterministic network models in operations research
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