A general framework for updating belief distributions

We propose a framework for general Bayesian inference. We argue that a valid update of a prior belief distribution to a posterior can be made for parameters which are connected to observations through a loss function rather than the traditional likelihood function, which is recovered as a special ca...

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Bibliographic Details
Published in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 78; no. 5; pp. 1103 - 1130
Main Authors:	Bissiri, P. G., Holmes, C. C., Walker, S. G.
Format:	Journal Article
Language:	English
Published:	England Blackwell Publishing Ltd 01.11.2016 John Wiley & Sons Ltd Oxford University Press John Wiley and Sons Inc
Subjects:	Bayesian analysis Bayesian theory Beliefs Coherence Data Decision theory Density equations Frame analysis General Bayesian updating Generalized estimating equations Gibbs posteriors Inference Information Joints Learning Loss function Mathematical models Maximum entropy Original Parameters probability Provably approximately correct Bayes methods Self-information loss function Statistics Studies Subjectivity Loss function Self‐information loss function Decision theory Provably approximately correct Bayes methods General Bayesian updating Gibbs posteriors Information Maximum entropy Generalized estimating equations
ISSN:	1369-7412, 1467-9868
Online Access:	Get full text
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