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...
| Автори: | , , |
|---|---|
| Формат: | Journal article |
| Опубліковано: |
Wiley
2015
|