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Learning structures from data and experts

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  • Højsgaard, Søren

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.

Suggested Citation

  • Højsgaard, Søren, 1996. "Learning structures from data and experts," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 42(2), pages 143-152.
    • Handle: RePEc:eee:matcom:v:42:y:1996:i:2:p:143-152
    DOI: 10.1016/0378-4754(95)00122-0
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    References listed on IDEAS

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    1. Hojsgaard, Soren & Thiesson, Bo, 1995. "BIFROST -- Block recursive models induced from relevant knowledge, observations, and statistical techniques," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 155-175, February.
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