Description Usage Arguments Details Value References See Also

Generate a sample from a probability distribution with slice sampling with univariate steps along eigenvectors.

1 2 3 4 | ```
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 |

`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 |

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`

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