An approach for improving the sampling efficiency in the Bayesian calibration of computationally expensive simulation models

[1] In recent years, interest in the Bayesian approach for the calibration of hydrological and environmental simulation (HES) models has been growing. To extract useful information on unknown parameters produced in a Bayesian calibration, it is often necessary to rely on samples drawn from the posterior distribution. Sampling a posterior distribution requires a large number of evaluations of the simulation model, and the total computational costs could be prohibitively high when the simulation model is computationally expensive. A new computing strategy is proposed in this paper to alleviate this computational difficulty by making better use of the information generated in a costly run of the HES model by using multiple evaluations of the posterior density in the less computationally expensive subspace of error model parameters. A multiple-try Markov chain Monte Carlo (MCMC) algorithm is designed to implement this idea and is benchmarked with the Metropolis-Hastings algorithm, a basic recipe for MCMC sampling. The results show that the proposed strategy has potential for improving the computational efficiency of posterior sampling and easing its implementation in the Bayesian calibration of computationally expensive HES models.