Description Usage Arguments Details Value Note Author(s) References See Also

Draws samples from a log-concave maximum likelihood
estimate. The estimate should be specified in the form of an object of
class `"LogConcDEAD"`

, the result of a call to
`mlelcd`

.

1 |

`n` |
A scalar integer indicating the number of samples required |

`lcd` |
Object of class |

`method` |
Indicator of the method used to draw samples, either via independent rejection sampling (default choice) or via Metropolis-Hastings |

This function by default uses a simple rejection sampling scheme to draw independent random samples from a log-concave maximum likelihood estimator. One can also use the Metropolis-Hastings option to draw (dependent) samples with a higher acceptance rate.

For examples, see `mlelcd`

.

A numeric `matrix`

with `nsample`

rows, each row corresponding to a point
in *R^d* drawn from the distribution with density defined by `lcd`

.

Details of the rejection sampling can be found in Appendix B.3 of Cule, Samworth and Stewart (2010). Details of the Metropolis-Hastings scheme can be found in Gopal and Casella (2010)

Yining Chen

Madeleine Cule

Robert Gramacy

Richard Samworth

Cule, M. L., Samworth, R. J., and Stewart, M. I. (2010)
*Maximum likelihood estimation of a multi-dimensional log-concave density*
J. Roy. Statist. Soc., Ser. B. (with discussion), 72, 545-600.

Gopal, V. and Casella, G. (2010)
*Discussion of Maximum likelihood estimation of a log-concave density by Cule, Samworth and Stewart*
J. Roy. Statist. Soc., Ser. B., 72, 580-582.

LogConcDEAD documentation built on April 13, 2018, 9:04 a.m.

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