Elicitation by design in ecology: using expert opinion to inform priors for Bayesian statistical models
Low Choy, Samantha J., O'Leary, Rebecca A., & Mengersen, Kerrie L. (2009) Elicitation by design in ecology: using expert opinion to inform priors for Bayesian statistical models. Ecology, 90(1), pp. 265-277.
Bayesian statistical modeling has several benefits within an ecological context. In particular, when observed data are limited in sample size or representativeness, then the Bayesian framework provides a mechanism to combine observed data with other "prior" information. Prior information may be obtained from earlier studies, or in their absence, from expert knowledge. This use of the Bayesian framework reflects the scientific "learning cycle," where prior or initial estimates are updated when new data become available. In this paper we outline a framework for statistical design of expert elicitation processes for quantifying such expert knowledge, in a form suitable for input as prior information into Bayesian models. We identify six key elements: determining the purpose and motivation for using prior information; specifying the relevant expert knowledge available; formulating the statistical model; designing effective and efficient numerical encoding; managing uncertainty; and designing a practical elicitation protocol. We demonstrate this framework applies to a variety of situations, with two examples from the ecological literature and three from our experience. Analysis of these examples reveals several recurring important issues affecting practical design of elicitation in ecological problems.
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|Item Type:||Journal Article|
|Additional Information:||For more information, please refer to the journal's website (see hypertext link) or contact the author.|
|Keywords:||design, expert elicitation, ecology, informative Bayesian analysis, prior, framework|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > PSYCHOLOGY AND COGNITIVE SCIENCES (170000) > COGNITIVE SCIENCE (170200) > Knowledge Representation and Machine Learning (170203)
Australian and New Zealand Standard Research Classification > ENVIRONMENTAL SCIENCES (050000) > ECOLOGICAL APPLICATIONS (050100)
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
|Copyright Owner:||Copyright 2009 Ecological Society of America|
|Deposited On:||10 Feb 2009 23:33|
|Last Modified:||02 Sep 2012 22:59|
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