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.

On general Bayesian inference using loss functions

Bissiri P. G.;
2019

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.
STATISTICS & PROBABILITY LETTERS
Bissiri P.G.; Walker S.G.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/709760
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