A0549
Title: Bayesian score calibration for approximate models
Authors: Christopher Drovandi - Queensland University of Technology (Australia) [presenting]
Joshua Bon - Queensland University of Technology (Australia)
David Nott - National University of Singapore (Singapore)
David Warne - Queensland University of Technology (Australia)
Abstract: Scientists continue to develop increasingly complex mechanistic models to reflect their knowledge more realistically. Statistical inference using these models can be highly challenging since the corresponding likelihood function is often intractable, and model simulation may be computationally burdensome. Fortunately, in many of these situations, it is possible to adopt a surrogate model or approximate likelihood function. It may be convenient to base Bayesian inference directly on the surrogate, but this can result in bias and poor uncertainty quantification. Here a new method for adjusting approximate posterior samples is proposed to reduce bias and produce more accurate uncertainty quantification. This is done by optimising a transform of the approximate posterior that maximises a scoring rule. Our approach requires only a (fixed) small number of complex model simulations and is numerically stable. The good performance of the new method on several examples of increasing complexity is demonstrated.
