Differential Evolution Markov Chain with snooker updater and fewer chains
Statistics and Computing
Adaptive independence samplers
Statistics and Computing
Computational Statistics & Data Analysis
Influence of crossover on the behavior of Differential Evolution Algorithms
Applied Soft Computing
Distributed evolutionary Monte Carlo for Bayesian computing
Computational Statistics & Data Analysis
Geometrical recombination operators for real-coded evolutionary mcmcs
Evolutionary Computation
CODEQ: an effective metaheuristic for continuous global optimisation
International Journal of Metaheuristics
MOMCMC: An efficient Monte Carlo method for multi-objective sampling over real parameter space
Computers & Mathematics with Applications
Panel discussion: integrating data from multiple simulation models of different fidelity
Proceedings of the Winter Simulation Conference
Parameter identification of the STICS crop model, using an accelerated formal MCMC approach
Environmental Modelling & Software
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Differential Evolution (DE) is a simple genetic algorithm for numerical optimization in real parameter spaces. In a statistical context one would not just want the optimum but also its uncertainty. The uncertainty distribution can be obtained by a Bayesian analysis (after specifying prior and likelihood) using Markov Chain Monte Carlo (MCMC) simulation. This paper integrates the essential ideas of DE and MCMC, resulting in Differential Evolution Markov Chain (DE-MC). DE-MC is a population MCMC algorithm, in which multiple chains are run in parallel. DE-MC solves an important problem in MCMC, namely that of choosing an appropriate scale and orientation for the jumping distribution. In DE-MC the jumps are simply a fixed multiple of the differences of two random parameter vectors that are currently in the population. The selection process of DE-MC works via the usual Metropolis ratio which defines the probability with which a proposal is accepted. In tests with known uncertainty distributions, the efficiency of DE-MC with respect to random walk Metropolis with optimal multivariate Normal jumps ranged from 68% for small population sizes to 100% for large population sizes and even to 500% for the 97.5% point of a variable from a 50-dimensional Student distribution. Two Bayesian examples illustrate the potential of DE-MC in practice. DE-MC is shown to facilitate multidimensional updates in a multi-chain "Metropolis-within-Gibbs" sampling approach. The advantage of DE-MC over conventional MCMC are simplicity, speed of calculation and convergence, even for nearly collinear parameters and multimodal densities.