Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.