oblique.hyperrect.sample: Eigendecomposition-based hyperrectangle method
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 the hyperrectangle method with slice approximation axes oriented along eigenvectors.

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oblique.hyperrect.sample(target.dist, x0, sample.size, tuning=1,
                    edge.scale=5, cheat=FALSE)
cheat.oblique.hyperrect.sample(target.dist, x0, sample.size, tuning=1)

`target.dist`	Target distribution; see `make.dist`.
`x0`	Numeric vector containing initial state.
`sample.size`	Sample size requested.
`tuning`	Scale of initial/fallback hypercube edge; `w` in Thompson (2011, ch. 3).
`edge.scale`	The initial slice approximation has edges of length equal to the square root of the corresponding eigenvalue times this factor.
`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 the hyperrectangle method (Neal, 2003, sec. 5.1) with the hyperrectangle oriented along estimates of the eigenvectors of the target distribution's covariance, as described by Thompson (2011, ch. 3). The functions follow the interface used by compare.samplers. Calling cheat.oblique.hyperrect.sample is equivalent to calling oblique.hyperrect.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.

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

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

compare.samplers, univar.eigen.sample

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