Learning structures from data and experts

Søren Højsgaard

Mathematics and Computers in Simulation (MATCOM), 1996, vol. 42, issue 2, 143-152

Abstract: In modelling complex stochastic systems graphical association models provide a convenient framework. With graphical models the overall structure of association among variables is described in terms of conditional independence, and this structure can be represented graphically. Statistical methods for revealing these basic structures of association on the basis of data and expert knowledge are described. Suggestions on how to make a more detailed modelling will be made, and it will be illustrated how to implement such models in a causal probabilistic network. As an illustration a model for the incidence of fungi attacks and yield in relation to various cultural factors in winter wheat is established.

Date: 1996
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:42:y:1996:i:2:p:143-152

DOI: 10.1016/0378-4754(95)00122-0

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