Dakota Reference Manual  Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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DREAM (DiffeRential Evolution Adaptive Metropolis)


This keyword is related to the topics:


Alias: none

Argument(s): none

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
Required chain_samples

Number of Markov Chain Monte Carlo posterior samples

Optional seed

Seed of the random number generator

Optional chains Number of chains in DREAM
Optional num_cr Number of candidate points for each crossover.
Optional crossover_chain_pairs Number of chains used in crossover.
Optional gr_threshold Convergence tolerance for the Gelman-Rubin statistic
Optional jump_step Number of generations a long jump step is taken
Optional emulator

Use an emulator or surrogate model to evaluate the likelihood function

Optional export_chain_points_file

Export the MCMC chain to the specified filename


The DiffeRential Evolution Adaptive Metropolis algorithm is described in[86]. For the DREAM method, one can define the number of chains used with chains (minimum 3). The total number of generations per chain in DREAM is the number of samples (samples) divided by the number of chains (chains). The number of chains randomly selected to be used in the crossover each time a crossover occurs is crossover_chain_pairs. There is an extra adaptation during burn-in, in which DREAM estimates a distribution of crossover probabilities that favors large jumps over smaller ones in each of the chains. Normalization is required to ensure that all of the input dimensions contribute equally. In this process, a discrete number of candidate points for each crossover value is generated. This parameter is num_cr. The gr_threshold is the convergence tolerance for the Gelman-Rubin statistic which will govern the convergence of the multiple chain process. The integer jump_step forces a long jump every jump_step generations. For more details about these parameters, see[86].

Attention: While the emulator specification for DREAM includes the keyword posterior_adaptive, it is not yet operational.