DREAM1
Markov Chain Monte Carlo acceleration by Differential Evolution


DREAM1 is a C library which is an older implementation of the DREAM algorithm for accelerating Markov Chain Monte Carlo (MCMC) convergence using differential evolution, by Guannan Zhang.

Using the DREAM1 library to solve a problem requires:

A more recent version of the algorithm, with a simplified interface involving 5 user functions, is available as DREAM.

Web Link:

A version of the DREAM library is available in http://tasmanian.ornl.gov, the TASMANIAN library, available from Oak Ridge National Laboratory.

Licensing:

The computer code and data files described and made available on this web page are distributed under the GNU LGPL license.

Languages:

DREAM1 is available in a C version and a C++ version and a FORTRAN90 version.

Related Data and Programs:

DREAM, a C program which implements the DREAM algorithm for accelerating Markov Chain Monte Carlo (MCMC) convergence using differential evolution, using five user functions to define the problem, by Guannan Zhang.

RANLIB, a C library which produces random samples from Probability Density Functions (PDF's), including Beta, Chi-square Exponential, F, Gamma, Multivariate normal, Noncentral chi-square, Noncentral F, Univariate normal, random permutations, Real uniform, Binomial, Negative Binomial, Multinomial, Poisson and Integer uniform, by Barry Brown and James Lovato.

RNGLIB, a C library which implements a random number generator (RNG) with splitting facilities, allowing multiple independent streams to be computed, by L'Ecuyer and Cote.

Author:

Original FORTRAN90 version by Guannan Zhang; C version by John Burkardt.

Reference:

  1. Pierre LEcuyer, Serge Cote,
    Implementing a Random Number Package with Splitting Facilities,
    ACM Transactions on Mathematical Software,
    Volume 17, Number 1, March 1991, pages 98-111.
  2. Jasper Vrugt, CJF ter Braak, CGH Diks, Bruce Robinson, James Hyman, Dave Higdon,
    Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling,
    International Journal of Nonlinear Sciences and Numerical Simulation,
    Volume 10, Number 3, March 2009, pages 271-288.

Source Code:

Examples and Tests:

Input files:

Output files:

List of Routines:

You can go up one level to the C source codes.


Last revised on 30 April 2013.