Covariance-Adaptive Slice Sampling

Madeleine Thompson, Dept. of Statistics, University of Toronto
Radford M. Neal, Dept. of Statistics and Dept. of Computer Science, University of Toronto

We describe two slice sampling methods for taking multivariate steps using the crumb framework. These methods use the gradients at rejected proposals to adapt to the local curvature of the log-density surface, a technique that can produce much better proposals when parameters are highly correlated. We evaluate our methods on four distributions and compare their performance to that of a non-adaptive slice sampling method and a Metropolis method. The adaptive methods perform favorably on low-dimensional target distributions with highly-correlated parameters.

Technical Report No. 1002, Dept. of Statistics, University of Toronto (March 2010), 17 pages: postscript, pdf.

The R programs accompanying this paper are here.