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Prior Elicitation: Interactive Spreadsheet Graphics With Sliders Can Be Fun, and Informative

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  • Geoffrey Jones
  • Wesley O. Johnson

Abstract

There are several approaches to setting priors in Bayesian data analysis. Some attempt to minimize the impact of the prior on the posterior, allowing the data to "speak for themselves," or to provide Bayesian inferences that have good frequentist properties. In contrast, this note focuses on priors where scientific knowledge is used, possibly partially informative. There are many articles on the use of such subjective information. We focus on using standard software for eliciting priors from subject-matter specialists, in the form of models such as the binomial, Poisson, and normal. Our approach uses a common spreadsheet package with the facility to display dynamic pictures of prior distributions as the user toggles scroll bars or "sliders" that manipulate parameters of particular distributions. This allows interactive exploration of the shape of a probability distribution. We have found this a useful tool when eliciting priors for Bayesian data analysis. We present examples to illustrate the scope and flexibility of the method. Supplementary materials for this article are available online.

Suggested Citation

  • Geoffrey Jones & Wesley O. Johnson, 2014. "Prior Elicitation: Interactive Spreadsheet Graphics With Sliders Can Be Fun, and Informative," The American Statistician, Taylor & Francis Journals, vol. 68(1), pages 42-51, February.
  • Handle: RePEc:taf:amstat:v:68:y:2014:i:1:p:42-51
    DOI: 10.1080/00031305.2013.868828
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    References listed on IDEAS

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    1. John W. Seaman & John W. Seaman & James D. Stamey, 2012. "Hidden Dangers of Specifying Noninformative Priors," The American Statistician, Taylor & Francis Journals, vol. 66(2), pages 77-84, May.
    2. Garthwaite, Paul H. & Kadane, Joseph B. & O'Hagan, Anthony, 2005. "Statistical Methods for Eliciting Probability Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 680-701, June.
    3. Little R.J., 2004. "To Model or Not To Model? Competing Modes of Inference for Finite Population Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 546-556, January.
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    Cited by:

    1. Julia R. Falconer & Eibe Frank & Devon L. L. Polaschek & Chaitanya Joshi, 2022. "Methods for Eliciting Informative Prior Distributions: A Critical Review," Decision Analysis, INFORMS, vol. 19(3), pages 189-204, September.
    2. Geoffrey Jones & Wesley O. Johnson, 2016. "A Bayesian Superpopulation Approach to Inference for Finite Populations Based on Imperfect Diagnostic Outcomes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(2), pages 314-327, June.
    3. David M. Rindskopf & William R. Shadish & M. H. Clark, 2018. "Using Bayesian Correspondence Criteria to Compare Results From a Randomized Experiment and a Quasi-Experiment Allowing Self-Selection," Evaluation Review, , vol. 42(2), pages 248-280, April.

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