On general Bayesian inference using loss functions

Publication date: Available online 8 May 2019Source: Statistics & Probability LettersAuthor(s): Pier Giovanni Bissiri, Stephen G. WalkerAbstractBissiri et al. (2016) propose a framework for general Bayesian inference using loss functions which connec 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.

Source: Statistics and Probability Letters - May 9, 2019 Category: Statistics Source Type: research