univar.eigen.sample: Eigendecomposition-based 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 slice sampling with univariate steps along eigenvectors.

univar.eigen.sample(target.dist, x0, sample.size, tuning=1,
                    steps.out=100, cheat=FALSE)
cheat.univar.eigen.sample(target.dist, x0, sample.size, tuning=1,
                    steps.out=100)

`target.dist`	Target distribution; see `make.dist`.
`x0`	Numeric vector containing initial state.
`sample.size`	Sample size requested.
`tuning`	Initial slice approximation length.
`steps.out`	Maximum number of iterations the stepping out algorithm should run when choosing an initial slice approximation. Set to NULL to refrain from stepping out.
`cheat`	Set to true to use the covariance from `target.dist` instead of estimating it. This is not possible on real problems but can be useful for debugging.

These two functions implement slice sampling with univariate steps along estimated eigenvectors. Thompson (2011, ch. 3) has details on the algorithms. The functions follow the interface used by compare.samplers. Calling cheat.univar.eigen.sample is equivalent to calling univar.eigen.sample with cheat=TRUE; it is provided as a convenience so that it can be passed directly to compare.samplers.

A list with elements X, evals, and grads. See compare.samplers for more information.

Thompson, M. B. (2011), Slice Sampling with Multivariate Steps. http://hdl.handle.net/1807/31955.

compare.samplers, oblique.hyperrect.sample

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