Ensemble preconditioning for Markov chain Monte Carlo simulation
This addresses sampling inefficiencies in high-dimensional statistical models, though it appears incremental as it builds on existing ensemble methods.
The paper tackled the problem of difficult anisotropic sampling in high dimensions by introducing parallel Markov chain Monte Carlo methods that use ensemble preconditioning, achieving dramatic potential speedups compared to alternative schemes.
We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighboring 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.