  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.
