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A general framework for updating belief distributions

Abstract:

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 case.

Modern application areas make it is increasingly challenging for Bayesians to attempt to model the true data generating mechanism. For instance, when the object of interest is low dim...

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Publication status:
Accepted
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Institution:
University of Oxford
Department:
Oxford, MSD, NDM, NDM Strategic
Role:
Author
Publisher:
Wiley Publisher's website
Journal:
Journal of the Royal Statistical Society Series B: Statistical Methodology Journal website
Publication date:
2015-01-01
ISSN:
1467-9868
URN:
uuid:dabf30b7-9576-47e4-a6cc-a6b0e7e51257
Source identifiers:
578812
Local pid:
pubs:578812

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