Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation

Bayesian inference has found widespread application and use in science and engineering to reconcile Earth system models with data, including prediction in space (interpolation), prediction in time (forecasting), assimilation of observations and deterministic/stochastic model out ut, and inference of the model parameters. Bayes theorem states that the posterior probability, p(H vertical bar(Y) over tilde) of a hypothesis, H is proportional to the product of the prior probability, p(H) of this hypothesis an the likelihood, L(H vertical bar(Y) over tilde) of the same hypothesis given the new observations, (Y) over tilde, or p(H vertical bar(Y) over tilde)proportional to p(H)L(H vertical bar(Y) over tilde). In science and engineering, H often constitutes some numerical model F(x) which summarizes, in algebraic and differential equations, state variables and fluxes, all knowledge of the system of interest, and the unknown parameter values, x are subject to inference using the data vertical bar(Y)over tilde>. Unfortunately, for complex system models the posterior distribution is often high dimensional and analytically intractable, and sampling methods are required to approximate the target. In this paper I review the basic theory of Markov chain Monte Carlo (MCMC) simulation and introduce a MATLAB toolbox of the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm developed by Vrugt at al. (2008a, 2009a) and used for Bayesian inference in fields ranging from physics, chemistry and engineering, to ecology, hydrology, and geophysics. This MATLAB toolbox provides scientists and engineers with an arsenal of options and utilities to solve posterior sampling problems involving (among others) bimodality, high-dimensionality, summary statistics, bounded parameter spaces, dynamic simulation models, formal/informal likelihood functions (GLUE), diagnostic model evaluation, data assimilation, Bayesian model averaging, distributed computation, and informative/non-informative prior distributions. The DREAM toolbox supports parallel computing and includes tools for convergence analysis of the sampled chain trajectories and post-processing of the results. Seven different case studies illustrate the main capabilities and functionalities of the MATLAB toolbox. (C) 2015 Elsevier Ltd. All rights reserved.