shrinking.rank.sample

Generate a sample from a probability distribution with
 the shrinking-rank slice sampling method.

A framework for running sets of MCMC samplers on sets of distributions with a variety of tuning parameters, along with plotting functions to visualize the results of those simulations.

Madeleine Thompson

SamplerCompare

A Framework for Comparing the Performance of MCMC Samplers

shrinking.rank.sample function

<dl> <dt>target.dist</dt>
<dd>Target distribution; see <code>make.dist</code>.</dd> <dt>x0</dt>
<dd>Numeric vector containing initial state.</dd> <dt>sample.size</dt>
<dd>Requested sample size.</dd> <dt>tuning</dt>
<dd>A tuning parameter; corresponds to \(\sigma_c\)
 in sec. 5 of Thompson and Neal (2010).</dd> <dt>downscale</dt>
<dd>Factor to reduce crumb standard deviation by when a
 proposal is rejected.</dd> <dt>min.dimension</dt>
<dd>The minimum dimension to sample crumbs from.</dd></dl>

Arguments

Sample with shrinking-rank slice sampling — shrinking.rank.sample

<dl>

 <dt>target.dist</dt>
<dd>Target distribution; see <code>make.dist</code>.</dd>

 <dt>x0</dt>
<dd>Numeric vector containing initial state.</dd>

 <dt>sample.size</dt>
<dd>Requested sample size.</dd>

 <dt>tuning</dt>
<dd>A tuning parameter; corresponds to \(\sigma_c\)
 in sec. 5 of Thompson and Neal (2010).</dd>

 <dt>downscale</dt>
<dd>Factor to reduce crumb standard deviation by when a
 proposal is rejected.</dd>

 <dt>min.dimension</dt>
<dd>The minimum dimension to sample crumbs from.</dd>

</dl>

shrinking.rank.sample: Sample with shrinking-rank slice sampling

Description

Usage

Value

Arguments

Details

References

See Also