Iterative construction of Gaussian process surrogate models for Bayesian inference

• Published in: Journal of statistical planning and inference Vol. 207; no. C; pp. 55 - 72
• Main Authors: Alawieh, Leen, Goodman, Jonathan, Bell, John B.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2020; Elsevier
• Subjects: Active learning; Bayesian inference; Gaussian process regression; MCMC; Surrogate models; Gaussian process regression; MCMC; Surrogate models; Bayesian inference; Active learning
• DOI: 10.1016/j.jspi.2019.11.002
• ISSN: 0378-3758

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. •An adaptive emulator is built using Gaussian process regression.•An acquisition function is derived for optimal training of the emulator.•MCMC sampler is used to optimize the acquisition function.•Emulator is used to reduce computational cost of a Bayesian inverse problem.