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Non-Parametric Continuous Bayesian Belief Nets with Expert Judgement

  • Dorota Kurowicka
  • Roger Cooke

Abstract

This report introduces continuous belief nets using the vine — copulae modelling approach. Nodes are associated with continuous distributions, influences are associated with (conditional) rank correlations and are realized by (conditional) copulae. Any copula which represents (conditional) independence as zero (conditional) correlation can be used. We present an elicitation protocol based on (conditional) rank correlations and show how a unique joint distribution preserving the conditional independence properties of the Bayesian belief net can be determined, sampled and updated.

Keywords

Conditional Distribution Copula Modelling Influence Diagram Sampling Order Chance Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Bedford T.J. and Cooke R.M.. Vines — a new graphical model for dependent random variables. Ann. Stat. 2002; 30 no 4: 1031–1068.CrossRefMATHMathSciNetGoogle Scholar
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    Kurowicka D. and Cooke R.M. The vine copula method for representing high dimensional dependent distributions; application to continuous belief nets. Proc. Winter Simulation Conference. Yucesan et al (eds) 2002.Google Scholar
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    Cooke R.M. Markov and entropy properties of tree and vines-dependent variables, Proc. of the ASA Section of Bayesian Statistical Science. 1997.Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • Dorota Kurowicka
    • 1
  • Roger Cooke
    • 1
  1. 1.Delft Institute of Applied MathematicsDelft University of TechnologyDelftThe Netherlands

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