Draws samples from a smoothed log-concave maximum likelihood
estimate. The estimate should be specified in the form of an object of
"LogConcDEAD", the result of a call to
and a positive definite matrix.
A scalar integer indicating the number of samples required
Object of class
A positive definite
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 smoothed 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
n rows, each row corresponding to a point
in R^d drawn from the distribution with density defined by
Chen, Y. and Samworth, R. J. (2013) Smoothed log-concave maximum likelihood estimation with applications Statist. Sinica, 23, 1373-1398. http://arxiv.org/abs/1102.1191v4
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.
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