I'm allergic to paywalled journals, but there's also an
old-fashioned double-spaced arXiv paper (probably required
for the submission to the old-fashioned paywalled journal):
http://arxiv.org/abs/1402.1694
But listen to Michael. Approximating a function and its gradients in
high dimensions is hard! Note the qualification on p. 28 of this paper,
which uses GPs to approximate densities:
Finding nearest neighbors is a hard problem asymptotically
with respect to the parameter dimension and size of the sample set,
but our sample sets are neither high dimensional nor large.
The nice thing about HMC is that it scales well with dimensionality.
- Bob