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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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Fig. 4.1
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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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