Inverse Gaussian Process regression for likelihood-free inference

Hongqiao Wang; Ziqiao Ao; Tengchao Yu; Jinglai Li

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arXiv · 2102.10583

Inverse Gaussian Process regression for likelihood-free inference

Hongqiao Wang·Ziqiao Ao·Tengchao Yu·Jinglai Li

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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.

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BibTeX RIS

Hongqiao Wang, Ziqiao Ao, Tengchao Yu, Jinglai Li. 2021-02-21. Inverse Gaussian Process regression for likelihood-free inference. https://arxiv.org/abs/2102.10583

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