shrinking.rank.sample: Sample with shrinking-rank slice sampling

In SamplerCompare: A Framework for Comparing the Performance of MCMC Samplers

Sample with shrinking-rank slice sampling

Description

Generate a sample from a probability distribution with the shrinking-rank slice sampling method.

Usage

shrinking.rank.sample(target.dist, x0, sample.size, tuning=1,
         downscale=0.95, min.dimension=1)

Arguments

target.dist	Target distribution; see make.dist.
x0	Numeric vector containing initial state.
sample.size	Requested sample size.
tuning	A tuning parameter; corresponds to \sigma_c in sec. 5 of Thompson and Neal (2010).
downscale	Factor to reduce crumb standard deviation by when a proposal is rejected.
min.dimension	The minimum dimension to sample crumbs from.

Details

shrinking.rank.slice.sample implements the shrinking-rank method of slice sampling, as described by Thompson and Neal (2010). It can be passed to compare.samplers in the samplers list argument.

Value

A list with elements X, evals, and grads, following the calling convention of compare.samplers.

References

Thompson, M. B. and Neal, R. M. (2010). Covariance-adaptive slice sampling. Technical Report TR-1002, Dept. of Statistics, University of Toronto.

See Also

compare.samplers cov.match.sample

SamplerCompare documentation built on April 24, 2023, 9:09 a.m.