stepout.slice.sample: Univariate slice samplers
In SamplerCompare: A Framework for Comparing the Performance of MCMC Samplers

Description Usage Arguments Details Value References See Also

Generate a sample from a probability distribution with a slice sampler.

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stepout.slice.sample(target.dist, x0, sample.size, tuning=1,
                     step.out=TRUE, limit=length(x0)*100)
interval.slice.sample(...)

`target.dist`	Target distribution; see `make.dist`.
`x0`	Numeric vector containing initial state.
`sample.size`	Requested sample size.
`tuning`	Initial interval length for slice.
`step.out`	Flag indicating whether to expand the initial interval before proposing a new coordinate.
`limit`	A limit on the number of log-density evaluations per observation before sampling is aborted.
`...`	`interval.slice.sample` takes the same arguments as `stepout.slice.sample`, except `step.out`.

stepout.slice.sample implements univariate slice sampling with stepping out as described in sec. 4 of Neal (2003). If step.out=FALSE or interval.slice.sample is called instead, no stepping out is performed; the wrapper function interval.slice.sample is provided for convenience when calling compare.samplers.

If target.dist is a multivariate distribution, each step of the Markov chain updates each coordinate once in sequence.

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

Neal, Radford M. (2003), “Slice Sampling,” The Annals of Statistics 31(3):705-767.

compare.samplers, hyperrectangle.sample

SamplerCompare documentation built on Oct. 22, 2021, 9:14 a.m.