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.
Bissiri P.G., Walker S.G. (2019). On general Bayesian inference using loss functions. STATISTICS & PROBABILITY LETTERS, 152, 89-91 [10.1016/j.spl.2019.04.005].
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.File in questo prodotto:
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