Many probabilistic models introduce strong dependencies between variables
using a latent multivariate Gaussian distribution or a Gaussian process. We
present a new Markov chain Monte Carlo algorithm for performing inference in
models with multivariate Gaussian priors. Its key properties are: 1) it has
simple, generic code applicable to many models, 2) it has no free parameters,
3) it works well for a variety of Gaussian process based models. These
properties make our method ideal for use while model building, removing the
need to spend time deriving and tuning updates for more complex algorithms.Comment: 8 pages, 6 figures, appearing in AISTATS 2010 (JMLR: W&CP volume 6).
Differences from first submission: some minor edits in response to feedback
