On general Bayesian inference using loss functions

Pier Giovanni Bissiri and Stephen G. Walker

Statistics & Probability Letters, 2019, vol. 152, issue C, 89-91

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.

Keywords: Conditional probability distribution; Decision theory; Information; Self-information loss function (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.spl.2019.04.005

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