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Uncertainty in prior elicitations: a nonparametric approach

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  • Jeremy E. Oakley
  • Anthony O'Hagan

Abstract

A key task in the elicitation of expert knowledge is to construct a distribution from the finite, and usually small, number of statements that have been elicited from the expert. These statements typically specify some quantiles or moments of the distribution. Such statements are not enough to identify the expert's probability distribution uniquely, and the usual approach is to fit some member of a convenient parametric family. There are two clear deficiencies in this solution. First, the expert's beliefs are forced to fit the parametric family. Secondly, no account is then taken of the many other possible distributions that might have fitted the elicited statements equally well. We present a nonparametric approach which tackles both of these deficiencies. We also consider the issue of the imprecision in the elicited probability judgements. Copyright 2007, Oxford University Press.

Suggested Citation

  • Jeremy E. Oakley & Anthony O'Hagan, 2007. "Uncertainty in prior elicitations: a nonparametric approach," Biometrika, Biometrika Trust, vol. 94(2), pages 427-441.
    • Handle: RePEc:oup:biomet:v:94:y:2007:i:2:p:427-441
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    File URL: http://hdl.handle.net/10.1093/biomet/asm031
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    Cited by:

    1. Michael D. Teter & Johannes O. Royset & Alexandra M. Newman, 2019. "Modeling uncertainty of expert elicitation for use in risk-based optimization," Annals of Operations Research, Springer, vol. 280(1), pages 189-210, September.
    2. Martin Forster & Emanuela Randon, 2020. "Epidemic policy under uncertainty and information," Discussion Papers 20/05, Department of Economics, University of York.
    3. Danila Azzolina & Paola Berchialla & Silvia Bressan & Liviana Da Dalt & Dario Gregori & Ileana Baldi, 2022. "A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method," IJERPH, MDPI, vol. 19(21), pages 1-15, October.
    4. Danila Azzolina & Paola Berchialla & Dario Gregori & Ileana Baldi, 2021. "Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review," IJERPH, MDPI, vol. 18(4), pages 1-21, February.
    5. Nicolas Bousquet, 2010. "Eliciting vague but proper maximal entropy priors in Bayesian experiments," Statistical Papers, Springer, vol. 51(3), pages 613-628, September.
    6. Lichtendahl Jr., Kenneth C., 2009. "Random quantiles of the Dirichlet process," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 501-507, February.
    7. Rebecca M. Turner & David J. Spiegelhalter & Gordon C. S. Smith & Simon G. Thompson, 2009. "Bias modelling in evidence synthesis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 21-47, January.

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