Generalized Bayesian Updating and the Loss-Likelihood Bootstrap

09/22/2017

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by   Simon Lyddon, et al.

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In this paper, we revisit the weighted likelihood bootstrap and show that it is well-motivated for Bayesian inference under misspecified models. We extend the underlying idea to a wider family of inferential problems. This allows us to calibrate an analogue of the likelihood function in situations where little is known about the data-generating mechanism. We demonstrate our method on a number of examples.