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On general Bayesian inference using loss functions

Lookup NU author(s): Dr Pier Giovanni Bissiri

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Abstract

© 2019 Elsevier B.V. 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.

Publication metadata

Author(s): Bissiri PG, Walker SG

Publication type: Article

Publication status: Published

Journal: Statistics and Probability Letters

Year: 2019

Volume: 152

Pages: 89-91

Print publication date: 01/09/2019

Online publication date: 08/05/2019

Acceptance date: 12/04/2019

ISSN (print): 0167-7152

Publisher: Elsevier BV * North-Holland

URL: https://doi.org/10.1016/j.spl.2019.04.005

DOI: 10.1016/j.spl.2019.04.005

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