core - DREAM core¶
Dream |
Data structure containing the details of the running DREAM analysis code. |
run_dream |
DiffeRential Evolution Adaptive Metropolis algorithm
DREAM runs multiple different chains simultaneously for global exploration, and automatically tunes the scale and orientation of the proposal distribution using differential evolution. The algorithm maintains detailed balance and ergodicity and works well and efficient for a large range of problems, especially in the presence of high-dimensionality and multimodality.
DREAM developed by Jasper A. Vrugt and Cajo ter Braak
This algorithm has been described in:
- Vrugt, J.A., C.J.F. ter Braak, M.P. Clark, J.M. Hyman, and B.A. Robinson,
- Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation, Water Resources Research, 44, W00B09, 2008. doi:10.1029/2007WR006720
- Vrugt, J.A., C.J.F. ter Braak, C.G.H. Diks, D. Higdon, B.A. Robinson,
- and J.M. Hyman, 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), 271-288, 2009.
- Vrugt, J.A., C.J.F. ter Braak, H.V. Gupta, and B.A. Robinson,
- Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling, Stochastic Environmental Research and Risk Assessment, 1-16, 2009, In Press. doi:10.1007/s00477-008-0274-y
For more information please read:
- Ter Braak, C.J.F.,
- A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces, Stat. Comput., 16, 239 - 249, 2006. doi:10.1007/s11222-006-8769-1
- Vrugt, J.A., H.V. Gupta, W. Bouten and S. Sorooshian,
- A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39 (8), 1201, 2003. doi:10.1029/2002WR001642
- Ter Braak, C.J.F., and J.A. Vrugt,
- Differential Evolution Markov Chain with snooker updater and fewer chains, Statistics and Computing, 2008. doi:10.1007/s11222-008-9104-9
- Vrugt, J.A., C.J.F. ter Braak, and J.M. Hyman,
- Differential evolution adaptive Metropolis with snooker update and sampling from past states, SIAM journal on Optimization, 2009.
- Vrugt, J.A., C.J.F. ter Braak, and J.M. Hyman,
- Parallel Markov chain Monte Carlo simulation on distributed computing networks using multi-try Metropolis with sampling from past states, SIAM journal on Scientific Computing, 2009.
- Schoups, and J.A. Vrugt,
A formal likelihood function for Bayesian inference of hydrologic models with correlated, heteroscedastic and non-Gaussian errors, Water Resources Research, 2010, In Press.
- Schoups, J.A. Vrugt, F. Fenicia, and N.C. van de Giesen,
Inaccurate numerical solution of hydrologic models corrupts efficiency and robustness of MCMC simulation, Water Resources Research, 2010, In Press.
Copyright (c) 2008, Los Alamos National Security, LLC All rights reserved.
Copyright 2008. Los Alamos National Security, LLC. This software was produced under U.S. Government contract DE-AC52-06NA25396 for Los Alamos National Laboratory (LANL), which is operated by Los Alamos National Security, LLC for the U.S. Department of Energy. The U.S. Government has rights to use, reproduce, and distribute this software.
NEITHER THE GOVERNMENT NOR LOS ALAMOS NATIONAL SECURITY, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is modified to produce derivative works, such modified software should be clearly marked, so as not to confuse it with the version available from LANL.
Additionally, redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
- Neither the name of Los Alamos National Security, LLC, Los Alamos National Laboratory, LANL the U.S. Government, nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY LOS ALAMOS NATIONAL SECURITY, LLC AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL LOS ALAMOS NATIONAL SECURITY, LLC OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
MATLAB code written by Jasper A. Vrugt, Center for NonLinear Studies (CNLS)
Written by Jasper A. Vrugt: vrugt@lanl.gov
Version 0.5: June 2008 Version 1.0: October 2008 Adaption updated and generalized CR implementation
2010-04-20 Paul Kienzle * Convert to python
-
class
bumps.dream.core.
Dream
(**kw)[source]¶ Bases:
object
Data structure containing the details of the running DREAM analysis code.
-
CR
= None¶
-
CR_spacing
= 'linear'¶
-
DE_eps
= 0.05¶
-
DE_noise
= 1e-06¶
-
DE_pairs
= 3¶
-
DE_snooker_rate
= 0.1¶
-
DE_steps
= 10¶
-
DR_scale
= 1¶
-
bounds_style
= 'reflect'¶
-
burn
= 0¶
-
draws
= 100000¶
-
goalseek_interval
= 1e+100¶
-
goalseek_minburn
= 1000¶
-
goalseek_optimizer
= None¶
-
model
= None¶
-
outlier_test
= 'IQR'¶
-
population
= None¶
-
sample
(state=None, abort_test=None)[source]¶ Pull the requisite number of samples from the distribution
-
thinning
= 1¶
-
use_delayed_rejection
= False¶
-