Covariance-Adaptive Slice Sampling

Details Covariance-Adaptive Slice Sampling Covariance-Adaptive Slice Sampling

2010-03-16

arxiv.org/abs/1003.3201v1

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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.

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