Inference in Complex Systems Using Multi-Phase MCMC Sampling With Gradient Matching Burn-in

摘要

We propose a novel method for parameter inference that builds on the current research in gradient matching surrogate likelihood spaces. Adopting a three phase technique, we demonstrate that it is possible to obtain parameter estimates of limited bias whilst still adopting the paradigm of the computationally cheap surrogate approximation.