uni.slice

Compute a draw from a univariate distribution using the code provided by
Radford M. Neal. The documentation below is also reproduced from Neal (2008).

Monte Carlo and MCMC sampling algorithms for semiparametric
Bayesian regression analysis. These models feature a nonparametric
(unknown) transformation of the data paired with widely-used
regression models including linear regression, spline regression,
quantile regression, and Gaussian processes. The transformation
enables broader applicability of these key models, including for
real-valued, positive, and compactly-supported data with challenging
distributional features. The samplers prioritize computational
scalability and, for most cases, Monte Carlo (not MCMC) sampling for
greater efficiency. Details of the methods and algorithms are provided
in Kowal and Wu (2023) <arXiv:2306.05498>.

Dan Kowal

SeBR

Semiparametric Bayesian Regression Analysis

uni.slice function

<dl><dt>x0</dt>
<dd>Initial point</dd>
<dt>g</dt>
<dd>Function returning the log of the probability density (plus constant)</dd>
<dt>w</dt>
<dd>Size of the steps for creating interval (default 1)</dd>
<dt>m</dt>
<dd>Limit on steps (default infinite)</dd>
<dt>lower</dt>
<dd>Lower bound on support of the distribution (default -Inf)</dd>
<dt>upper</dt>
<dd>Upper bound on support of the distribution (default +Inf)</dd>
<dt>gx0</dt>
<dd>Value of g(x0), if known (default is not known)</dd></dl>

Arguments

Univariate Slice Sampler from Neal (2008) — uni.slice

<dl>

<dt>x0</dt>
<dd>Initial point</dd>


<dt>g</dt>
<dd>Function returning the log of the probability density (plus constant)</dd>


<dt>w</dt>
<dd>Size of the steps for creating interval (default 1)</dd>


<dt>m</dt>
<dd>Limit on steps (default infinite)</dd>


<dt>lower</dt>
<dd>Lower bound on support of the distribution (default -Inf)</dd>


<dt>upper</dt>
<dd>Upper bound on support of the distribution (default +Inf)</dd>


<dt>gx0</dt>
<dd>Value of g(x0), if known (default is not known)</dd>

</dl>

uni.slice: Univariate Slice Sampler from Neal (2008)

Description

Usage

Value

Arguments