# Bumps 0.9.1 documentation

## core - DREAM core

`Dream` | Data structure containing the details of the running DREAM analysis code.

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.

> 7. 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.

> 7. 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.

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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)

  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
  alpha = 0.01

    convergence criteria

  bounds_style = 'reflect'
  burn = 0
  draws = 100000
  goalseek_interval = 1e+100
  goalseek_minburn = 1000
  goalseek_optimizer = None
  model = None
  outlier_test = 'none'
  population = None
  sample(state=None, abort_test=<function Dream.<lambda>>)

    Pull the requisite number of samples from the distribution

  state: MCMCDraw = None
  thinning = 1
  use_delayed_rejection = False