Open Access
June 2003 Slice sampling
Radford M. Neal
Ann. Statist. 31(3): 705-767 (June 2003). DOI: 10.1214/aos/1056562461


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.


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Radford M. Neal. "Slice sampling." Ann. Statist. 31 (3) 705 - 767, June 2003.


Published: June 2003
First available in Project Euclid: 25 June 2003

zbMATH: 1051.65007
MathSciNet: MR1994729
Digital Object Identifier: 10.1214/aos/1056562461

Primary: 65C05 , 65C60

Keywords: Adaptive methods , auxiliary variables , dynamical methods , Gibbs sampling , Markov chain Monte Carlo , Metropolis algorithm , overrelaxation

Rights: Copyright © 2003 Institute of Mathematical Statistics

Vol.31 • No. 3 • June 2003
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