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On general Bayesian inference using loss functions

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  • Bissiri, Pier Giovanni
  • Walker, Stephen G.

Abstract

Bissiri et al. (2016) propose a framework for general Bayesian inference using loss functions which connect parameters with data, and the updated posterior distribution is characterized through a set of axioms. The result, which is restricted to finite probability spaces, is extended in this paper to spaces which are subsets of the real line.

Suggested Citation

  • Bissiri, Pier Giovanni & Walker, Stephen G., 2019. "On general Bayesian inference using loss functions," Statistics & Probability Letters, Elsevier, vol. 152(C), pages 89-91.
    • Handle: RePEc:eee:stapro:v:152:y:2019:i:c:p:89-91
    DOI: 10.1016/j.spl.2019.04.005
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    References listed on IDEAS

    as
    1. Pier Bissiri & Stephen Walker, 2012. "Converting information into probability measures with the Kullback–Leibler divergence," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(6), pages 1139-1160, December.
    2. P. G. Bissiri & C. C. Holmes & S. G. Walker, 2016. "A general framework for updating belief distributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 1103-1130, November.
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