Titlebook: Expert Systems and Probabilistic Network Models; Enrique Castillo,José Manuel Gutiérrez,Ali S. Hadi Book 1997 Springer-Verlag New York, In

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Grundlagen und Technologien des Ottomotors the relationships among them. We have also seen that all the information about the relationships among a set of variables is contained in the joint probability distribution (JPD) of the variables. Thus, the performance of a probabilistic expert system hinges on the correct specification of the JPD.

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https://doi.org/10.1007/978-3-658-25064-5is represented by a joint probability distribution (JPD) of the set of variables of interest. The JPD is needed for the knowledge base of probabilistic expert systems. We have also seen in Chapter 3 that the most general JPD involves an infeasible large number of parameters. For this reason, simplif

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Michael Fröhlich,Jochen Mayerl,Andrea Pieters associated with these methods. On one hand, some of these algorithms are not generally applicable to all types of network structures. For example, the polytrees algorithm (Section 8.3) applies only to networks with simple polytree structure. On the other hand, general exact propagation methods tha

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https://doi.org/10.1007/978-3-663-13346-9uire that the joint probability distribution (JPD) of the model be specified numerically, that is, all the parameters must be assigned fixed numeric values. However, numeric specification of these parameters may not be available, or it may happen that the subject-matter specialists can specify only

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