  We propose a hybrid Monte Carlo (HMC) technique applicable to
high-dimensional multivariate normal distributions that effectively samples
along chaotic trajectories. The method is predicated on the freedom of choice
of the HMC momentum distribution, and due to its mixing properties, exhibits
sample-to-sample autocorrelations that decay far faster than those in the
traditional hybrid Monte Carlo algorithm. We test the methods on distributions
of varying correlation structure, finding that the proposed technique produces
superior covariance estimates, is less reliant on step-size tuning, and can
even function with sparse or no momentum re-sampling. The method presented here
is promising for more general distributions, such as those that arise in
Bayesian learning of artificial neural networks and in the state and parameter
estimation of dynamical systems.
