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Hidden Dangers of Specifying Noninformative Priors

Author

Listed:
  • John W. Seaman
  • John W. Seaman
  • James D. Stamey

Abstract

“Noninformative” priors are widely used in Bayesian inference. Diffuse priors are often placed on parameters that are components of some function of interest. That function may, of course, have a prior distribution that is highly informative, in contrast to the joint prior placed on its arguments, resulting in unintended influence on the posterior for the function. This problem is not always recognized by users of “noninformative” priors. We consider several examples of this problem. We also suggest methods for handling such induced priors.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:amstat:v:66:y:2012:i:2:p:77-84
    DOI: 10.1080/00031305.2012.695938
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    Cited by:

    1. Robert F. Bordley, 2023. "Lessons for Decision-Analysis Practice from the Automotive Industry," Interfaces, INFORMS, vol. 53(3), pages 240-246, May.
    2. 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.
    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.
    4. Luigi Spezia, 2019. "Modelling covariance matrices by the trigonometric separation strategy with application to hidden Markov models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 399-422, June.
    5. Spezia, Luigi, 2020. "Bayesian variable selection in non-homogeneous hidden Markov models through an evolutionary Monte Carlo method," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).

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