Welcome to DREAM: global adaptive MCMC project!

DiffeRential Evolution Adaptive Metropolis (DREAM). Efficient global MCMC even in high-dimensional spaces. Developed by J.A. Vrugt, C.J.F. ter Braak et al.

The project summary page you can find here.

For information on how to use dream, please run in R:

help("dreamCalibrate") - to calibrate a function using dream
help("dream") - for low-level interface
demo(example1) - Fitting a banana shaped distribution
demo(example2) - Fitting an n-dimensional Gaussian distribution
demo(FME.nonlinear.model) - Calibrating the non-linear model shown in the FME package vignette
demo(FME.nonlinear.model_parallelisation) - Example of parallelisation using the SNOW package
demo(parallelisation_chain_id) - Example of parallelisation when DREAM calls an external model using batch files in separate folders.

To cite the DREAM algorithm please use:

Vrugt, J. A., ter Braak, C. J. F., Diks, C. G. H., Robinson, B. A., Hyman, J. M., Higdon, D., 2009. Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International Journal of Nonlinear Sciences and Numerical Simulation 10 (3), 273-290. DOI: 10.1515/IJNSNS.2009.10.3.273

To cite the dream package, please use:

Joseph Guillaume and Felix Andrews (2012). dream: DiffeRential Evolution Adaptive Metropolis. R package version 0.4-2. URL http://CRAN.R-project.org/package=dream

For additional information on the algorithm also see:

Vrugt, J. A., ter Braak, C. J. F., Gupta, H. V., Robinson, B. A., 2009. Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? Stochastic Environmental Research and Risk Assessment 23 (7), 1011--1026. DOI: 10.1007/s00477-008-0274-y
An example of parallelisation of DREAM for the SWAT model (ZIP file) can be downloaded. Thanks to John Joseph for this contribution. This example is documented in the publication:
Joseph, J.F., J.H.A. Guillaume (2013) Using a parallelized MCMC algorithm in R to identify appropriate likelihood functions for SWAT, Environmental Modelling & Software, 46, pp 292-298, DOI: 10.1016/j.envsoft.2013.03.012.

This implementation of DREAM has been tested against the original Matlab implementation. See example1.R and example2.R

Please note that the dream_zs and dream_d algorithms may be superior in your circumstances. These are not implemented in this package. Please read the following references for details:

Vrugt, J. A. and Ter Braak, C. J. F. (2011) DREAM(D): an adaptive Markov Chain Monte Carlo simulation algorithm to solve discrete, noncontinuous, and combinatorial posterior parameter estimation problems, Hydrol. Earth Syst. Sci., 15, 3701-3713, DOI: 10.5194/hess-15-3701-2011
ter Braak, C. and J. Vrugt (2008). Differential Evolution Markov Chain with snooker updater and fewer chains. Statistics and Computing 18(4): 435-446 DOI: 10.1007/s11222-008-9104-9
Laloy,E., and J.A. Vrugt. 2012. High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing. Water Resources Research, 48, W0156. DOI 10.1029/2011WR010608