Title: General Bayesian Updating and the Loss-Likelihood Bootstrap,
Abstract: In this paper we revisit the weighted likelihood bootstrap, a method that
generates samples from an approximate Bayesian posterior of a parametric model.
We show that the same method can be derived, without approximation, under a
Bayesian nonparametric model with the parameter of interest defined as
minimising an expected negative log-likelihood under an unknown sampling
distribution. This interpretation enables us to extend the weighted likelihood
bootstrap to posterior sampling for parameters minimizing an expected loss. We
call this method the loss-likelihood bootstrap. We make a connection between
this and general Bayesian updating, which is a way of updating prior belief
distributions without needing to construct a global probability model, yet
requires the calibration of two forms of loss function. The loss-likelihood
bootstrap is used to calibrate the general Bayesian posterior by matching
asymptotic Fisher information. We demonstrate the methodology on a number of
examples.