Invited comment on the paper "Slice Sampling" by Radford Neal

Walker, Stephen G. (2003) Invited comment on the paper "Slice Sampling" by Radford Neal. Annals of Statistics, 31 (3). pp. 755-758. ISSN 0090-5364. (The full text of this publication is not available from this repository)

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Official URL
http://dx.doi.org/10.1214/aos/1056562461

Abstract

Markov chain sampling methods that adapt to characteristics of the distribution being sampled can be constructed using the principle that one can ample from a distribution by sampling uniformly from the region under the plot of its density function. A Markov chain that converges to this uniform distribution can be constructed by alternating uniform sampling in the vertical direction with uniform sampling from the horizontal "slice" defined by the current vertical position, or more generally, with some update that leaves the uniform distribution over this slice invariant. Such "slice sampling" methods are easily implemented for univariate distributions, and can be used to sample from a multivariate distribution by updating each variable in turn. This approach is often easier to implement than Gibbs sampling and more efficient than simple Metropolis updates, due to the ability of slice sampling to adaptively choose the magnitude of changes made. It is therefore attractive for routine and automated use. Slice sampling methods that update all variables simultaneously are also possible. These methods can adaptively choose the magnitudes of changes made to each variable, based on the local properties of the density function. More ambitiously, such methods could potentially adapt to the dependencies between variables by constructing local quadratic approximations. Another approach is to improve sampling efficiency by suppressing random walks. This can be done for univariate slice sampling by "overrelaxation," and for multivariate slice sampling by "reflection" from the edges of the slice.

Item Type: Article
Uncontrolled keywords: Markov chain Monte Carlo; auxiliary variables; adaptive methods; Gibbs sampling; Metropolis algorithm; overrelaxation; dynamical methods
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Judith Broom
Date Deposited: 14 Mar 2009 07:48
Last Modified: 25 Jun 2014 10:53
Resource URI: http://kar.kent.ac.uk/id/eprint/10574 (The current URI for this page, for reference purposes)
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