Iterative Construction of Gaussian Process Surrogate Models for Bayesian Inference

no code implementations • 17 Nov 2019 • Leen Alawieh, Jonathan Goodman, John B. Bell

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.

Bayesian Inference