Inverse Gaussian Process regression for likelihood-free inference
Abstract
In this work we consider Bayesian inference problems with intractable likelihood functions. We present a method to compute an approximate of the posterior with a limited number of model simulations. The method features an inverse Gaussian Process regression (IGPR), i.e., one from the output of a simulation model to the input of it. Within the method, we provide an adaptive algorithm with a tempering procedure to construct the approximations of the marginal posterior distributions. With examples we demonstrate that IGPR has a competitive performance compared to some commonly used algorithms, especially in terms of statistical stability and computational efficiency, while the price to pay is that it can only compute a weighted Gaussian approximation of the marginal posteriors.
Cite
@article{arxiv.2102.10583,
title = {Inverse Gaussian Process regression for likelihood-free inference},
author = {Hongqiao Wang and Ziqiao Ao and Tengchao Yu and Jinglai Li},
journal= {arXiv preprint arXiv:2102.10583},
year = {2021}
}