swMATH ID: 23336
Software Authors: Leimkuhler, Benedict; Matthews, Charles; Weare, Jonathan
Description: Ensemble preconditioning for Markov chain Monte Carlo simulation. We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighbouring replicas. The use of collective dynamics eliminates multiplicative noise and stabilizes the dynamics, thus providing a practical approach to difficult anisotropic sampling problems in high dimensions. Numerical experiments with model problems demonstrate that dramatic potential speedups, compared to various alternative schemes, are attainable.
Homepage: https://bitbucket.org/c_matthews/ensembleqn
Keywords: stochastic sampling; Markov chain Monte Carlo; MCMC; computational statistics; machine learning; BFGS; Langevin methods; Brownian dynamics
Related Software: EnKF; Rtwalk; t-walk; emcee; BayesDA; Gridap; AlexNet; RMSprop; ImageNet; RMHMC; ACOR
Cited in: 12 Publications

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