Iterative Construction of Gaussian Process Surrogate Models for Bayesian Inference

TL;DR

This paper introduces an iterative Gaussian Process-based algorithm to efficiently approximate complex posterior distributions in Bayesian inverse problems, reducing reliance on traditional MCMC methods.

Contribution

The paper presents a novel iterative method combining Gaussian Processes with Gaussian proposals to better capture non-linearities in Bayesian inference, requiring fewer model simulations.

Findings

Accurately approximates non-Gaussian posteriors

Reduces number of forward model runs needed

Effective in reaction network parameter inference

Abstract

A new algorithm is developed to tackle the issue of sampling non-Gaussian model parameter posterior probability distributions that arise from solutions to Bayesian inverse problems. The algorithm aims to mitigate some of the hurdles faced by traditional Markov Chain Monte Carlo (MCMC) samplers, through constructing proposal probability densities that are both, easy to sample and that provide a better approximation to the target density than a simple Gaussian proposal distribution would. To achieve that, a Gaussian proposal distribution is augmented with a Gaussian Process (GP) surface that helps capture non-linearities in the log-likelihood function. In order to train the GP surface, an iterative approach is adopted for the optimal selection of points in parameter space. Optimality is sought by maximizing the information gain of the GP surface using a minimum number of forward model simulation runs. The accuracy of the GP-augmented surface approximation is assessed in two ways. The first consists of comparing predictions obtained from the approximate surface with those obtained through running the actual simulation model at hold-out points in parameter space. The second consists of a measure based on the relative variance of sample weights obtained from sampling the approximate posterior probability distribution of the model parameters. The efficacy of this new algorithm is tested on inferring reaction rate parameters in a 3-node and 6-node network toy problems, which imitate idealized reaction networks in combustion applications.