Fz_fun

Assuming a Gaussian latent data distribution (given x),
compute the CDF on a grid of points

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

Fz_fun function

<dl><dt>z</dt>
<dd>vector of points at which the CDF of z is evaluated</dd>
<dt>weights</dt>
<dd><code>n</code>-dimensional vector of weights; if NULL,
assume 1/n</dd>
<dt>mean_vec</dt>
<dd><code>n</code>-dimensional vector of means; if NULL,
assume mean zero</dd>
<dt>sd_vec</dt>
<dd><code>n</code>-dimensional vector of standard deviations</dd></dl>

Arguments

Compute the latent data CDF — Fz_fun

<dl>

<dt>z</dt>
<dd>vector of points at which the CDF of z is evaluated</dd>


<dt>weights</dt>
<dd><code>n</code>-dimensional vector of weights; if NULL,
assume 1/n</dd>


<dt>mean_vec</dt>
<dd><code>n</code>-dimensional vector of means; if NULL,
assume mean zero</dd>


<dt>sd_vec</dt>
<dd><code>n</code>-dimensional vector of standard deviations</dd>

</dl>

Fz_fun: Compute the latent data CDF

Description

Usage

Value

Arguments