A general framework for updating belief distributions
<p>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 s...
| Main Authors: | , , |
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| 格式: | Journal article |
| 出版: |
Wiley
2015
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