Picture water droplets

Developing mathematical theories of the physical world: Open Access research on fluid dynamics from Strathclyde

Strathprints makes available Open Access scholarly outputs by Strathclyde's Department of Mathematics & Statistics, where continuum mechanics and industrial mathematics is a specialism. Such research seeks to understand fluid dynamics, among many other related areas such as liquid crystals and droplet evaporation.

The Department of Mathematics & Statistics also demonstrates expertise in population modelling & epidemiology, stochastic analysis, applied analysis and scientific computing. Access world leading mathematical and statistical Open Access research!

Explore all Strathclyde Open Access research...

Multivariate elicitation : association, copulae, and graphical models

Daneshkhah, Alireza and Bedford, Tim (2011) Multivariate elicitation : association, copulae, and graphical models. In: Wiley Encyclopedia of Operations Research and Management Science. John Wiley & Sons Inc.. ISBN 9780470400630

Full text not available in this repository. Request a copy from the Strathclyde author

Abstract

In practical elicitation problems, we very often wish to elicit from the expert her knowledge about more than one uncertain quantity. When these are considered independent, the uncertainty about them is characterized simply by their marginal distributions; so it is sufficient to elicit the expert's knowledge about the quantities separately. When the independence assumption between the uncertain quantities of interest is not reasonable or has to be explored, the elicitation process becomes more complex. This is where we need to elicit information about the association between the variables in some way. Unfortunately, despite the growing literature about elicitation, this is an area where there is very little guidance to be found. In this article, we present some methods proposed in the literature to specify a multivariate distribution. We focus on the methods that use copulae and vines to construct a joint probability distribution. They enable us to build a multivariate distribution based on the elicited marginal distributions and their dependencies.