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Statistical Decision Theory pp 49–64Cite as

The Bayesian Paradigm

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Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Abstract

The Bayesian paradigm is founded on a probabilistic way of updating the prior information about the inferential targets (population quantities) by the information contained in the collected data.

Keywords

  • Posterior Distribution
  • Loss Function
  • Prior Distribution
  • Prior Information
  • Gray Zone

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  • Albert, J. (2009). Bayesian computation with R (2nd ed.). New York: Springer.

    CrossRef  MATH  Google Scholar 

  • Berry, D. (1996). Statistics: A Bayesian perspective. Belmont: Duxbury.

    Google Scholar 

  • Carlin, B. P., & Louis, T. A. (2008). Bayesian methods for data analysis (3rd ed.). Boca Raton: Chapman and Hall/CRC.

    MATH  Google Scholar 

  • Garthwaite, P. H., & O’Hagan, A. (2000). Quantifying expert opinion in the UK water industry: An experimental study. Journal of the Royal Statistical Society Ser D, 49, 455–477.

    CrossRef  Google Scholar 

  • Garthwaite, P. H., Kadane, J. B., & O’Hagan, A. (2005). Statistical methods for eliciting probability distributions. Journal of the American Statistical Association, 100, 680–701.

    MathSciNet  CrossRef  MATH  Google Scholar 

  • Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003). Bayesian data analysis (2nd ed.). Boca Raton: Chapman and Hall/CRC.

    Google Scholar 

  • Lee, P. M. (2004). Bayesian statistics: An introduction (3rd ed.). Chichester: Wiley.

    Google Scholar 

  • Longford, N. T. (2012). Comparing normal random samples, with uncertainty about the priors and utilities. Scandinavian Journal of Statistics, 39, 729–742.

    MathSciNet  CrossRef  MATH  Google Scholar 

  • Longford, N. T., & Andrade Bejarano, M. (2013). Decision theory for the variance ratio in ANOVA with random effects. Submitted.

    Google Scholar 

  • O’Hagan, A. (1998). Eliciting expert beliefs in substantial practical applications. Journal of the Royal Statistical Society Ser D, 47, 21–68.

    CrossRef  Google Scholar 

  • Robert, C. (2007). The Bayesian choice: From decision-theoretic foundations to computational implementation. New York: Springer.

    Google Scholar 

  • Smith, J. Q. (1988). Decision analysis. A Bayesian approach. New York: Oxford University Press.

    Google Scholar 

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Author information

Authors and Affiliations

  1. SNTL and Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain

    Nicholas T. Longford

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  1. Nicholas T. Longford
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Correspondence to Nicholas T. Longford .

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Longford, N.T. (2013). The Bayesian Paradigm. In: Statistical Decision Theory. SpringerBriefs in Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40433-7_4

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