Slice sampler
rnd = slicesample(initial,nsamples,'pdf',pdf)
rnd = slicesample(initial,nsamples,'logpdf',logpdf)
[rnd,neval]
= slicesample(initial,...)
[rnd,neval]
= slicesample(initial,...,Name,Value)
generates rnd
= slicesample(initial
,nsamples
,'pdf',pdf
)nsamples
random samples using the slice sampling method (see Algorithms). pdf
gives the target probability density function (pdf). initial
is a row vector or scalar containing the initial value of the random sample sequences.
generates samples using the logarithm of the pdf.rnd
= slicesample(initial
,nsamples
,'logpdf',logpdf
)
[
returns the average number of function evaluations that occurred in the slice sampling.rnd
,neval
]
= slicesample(initial
,...)
[
generates random samples with additional options specified by one or more rnd
,neval
]
= slicesample(initial
,...,Name,Value
)Name,Value
pair arguments.

Initial point, a scalar or row vector. Set 

Positive integer, the number of samples that 

Handle to a function that generates the probability density function, specified with 

Handle to a function that generates the logarithm of the probability density function, specified with 
Specify optional
commaseparated pairs of Name,Value
arguments. Name
is
the argument name and Value
is the corresponding value.
Name
must appear inside quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN
.

Nonnegative integer, the number of samples to generate and discard before generating the samples to return. The slice sampling algorithm is a Markov chain whose stationary distribution is proportional to that of the Default: 

Positive integer, where Default: 

Width of the interval around the current sample, a scalar or vector of positive values.
Default: 



Scalar, the mean number of function evaluations per sample.

There are no definitive suggestions for choosing appropriate values for burnin
, thin
, or width
. Choose starting values of burnin
and thin
, and increase them, if necessary, to give the requisite independence and marginal distributions. See Neal [1] for details of the effect of adjusting width
.
At each point in the sequence of random samples, slicesample
selects the next point by “slicing” the density to form a neighborhood around the previous point where the density is above some value. Consequently, the sample points are not independent. Nearby points in the sequence tend to be closer together than they would be from a sample of independent values. For many purposes, the entire set of points can be used as a sample from the target distribution. However, when this type of serial correlation is a problem, the burnin
and thin
parameters can help reduce that correlation.
slicesample
uses the slice sampling algorithm of Neal [1]. For numerical stability, it converts a pdf
function into a logpdf
function. The algorithm to resize the support region for each level, called “steppingout” and “steppingin,” was suggested by Neal.
[1] Neal, Radford M. "Slice Sampling." Ann. Stat. Vol. 31, No. 3, pp. 705–767, 2003. Available at Project Euclid.
mhsample
 rand
 randsample