Generalized Bayesian Updating and the Loss-Likelihood Bootstrap

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.