Combining elicited expert knowledge into Bayesian priors

Albert, Isabelle, Donnet, Sophie, Guihenneuc-Jouyaux, Chantal, Low-Choy, Samantha, Mengersen, Kerrie, & Rousseau, Judith (2013) Combining elicited expert knowledge into Bayesian priors. In Joint Statistical Meeting, 3-8 August 2013, Montréal, Québec, Canada. (Unpublished)

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Abstract

Information that is elicited from experts can be treated as data', so can be analysed using a Bayesian statistical model, to formulate a prior model. Typically methods for encoding a single expert's knowledge have been parametric, constrained by the extent of an expert's knowledge and energy regarding a target parameter. Interestingly these methods have often been deterministic, in that all elicited information is treated atface value', without error. Here we sought a parametric and statistical approach for encoding assessments from multiple experts. Our recent work proposed and demonstrated the use of a flexible hierarchical model for this purpose. In contrast to previous mathematical approaches like linear or geometric pooling, our new approach accounts for several sources of variation: elicitation error, encoding error and expert diversity. Of interest are the practical, mathematical and philosophical interpretations of this form of hierarchical pooling (which is both statistical and parametric), and how it fits within the subjective Bayesian paradigm. Case studies from a bioassay and project management (on PhDs) are used to illustrate the approach.

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ID Code: 61737
Item Type: Conference Item (Presentation)
Refereed: No
Additional Information: Please see Albert et al (2012) for the published paper that this presentation draws heavily on.
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 The Authors
Deposited On: 08 Aug 2013 22:58
Last Modified: 08 Aug 2013 22:58

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