Statistical Science

Discussion of “Bayesian Models and Methods in Public Policy and Government Settings” by S. E. Fienberg

David J. Hand

Full-text: Open access

Abstract

Fienberg convincingly demonstrates that Bayesian models and methods represent a powerful approach to squeezing illumination from data in public policy settings. However, no school of inference is without its weaknesses, and, in the face of the ambiguities, uncertainties, and poorly posed questions of the real world, perhaps we should not expect to find a formally correct inferential strategy which can be universally applied, whatever the nature of the question: we should not expect to be able to identify a “norm” approach. An analogy is made between George Box’s “no models are right, but some are useful,” and inferential systems.

Article information

Source
Statist. Sci., Volume 26, Number 2 (2011), 227-230.

Dates
First available in Project Euclid: 1 August 2011

Permanent link to this document
https://projecteuclid.org/euclid.ss/1312204011

Digital Object Identifier
doi:10.1214/11-STS331A

Mathematical Reviews number (MathSciNet)
MR2858385

Zentralblatt MATH identifier
1246.62047

Keywords
Inference modeling frequentist objective subjective

Citation

Hand, David J. Discussion of “Bayesian Models and Methods in Public Policy and Government Settings” by S. E. Fienberg. Statist. Sci. 26 (2011), no. 2, 227--230. doi:10.1214/11-STS331A. https://projecteuclid.org/euclid.ss/1312204011


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References

  • Bayarri, M. J. and Berger, J. O. (2004). The interplay of Bayesian and frequentist analysis. Statist. Sci. 19 58–80.
  • Cox, D. R. (2006). Principles of Statistical Inference. Cambridge Univ. Press, Cambridge.
  • Efron, B. (2010). Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Institute of Mathematical Statistics Monographs 1. Cambridge Univ. Press, Cambridge.
  • Fienberg, S. E. (2006). Does it make sense to be an “objective Bayesian”? Bayesian Anal. 1 429–432 (electronic).
  • Hand, D. J. (1994). Deconstructing statistical questions (with discussion). J. Roy. Statist. Soc. Ser. A 157 317–356.
  • Hand, D. J. (1996). Statistics and the theory of measurement (with discussion). J. Roy. Statist. Soc. Ser. A 159 445–492.
  • Royal Society (2010). Climate Change: A Summary of the Science. Royal Society, London.
  • Zhang, Z. (2007). Pattern discovery in adverse event data. Ph.D. thesis, Dept. Mathematics, Imperial College, London.

See also

  • Main article: Bayesian Models and Methods in Public Policy and Government Settings.