computeTimeRemaining

Estimate the remaining time in the algorithm

Monte Carlo 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 (2024) <doi:10.1080/01621459.2024.2395586>.

Dan Kowal

SeBR

Semiparametric Bayesian Regression Analysis

computeTimeRemaining function

<dl><dt>nsi</dt>
<dd>current iteration</dd>
<dt>timer0</dt>
<dd>initial timer value from <code>proc.time()[3]</code></dd>
<dt>nsims</dt>
<dd>total number of simulations</dd>
<dt>nprints</dt>
<dd>total number of printed updates</dd></dl>

Arguments

Estimate the remaining time in the algorithm — computeTimeRemaining

<dl>

<dt>nsi</dt>
<dd>current iteration</dd>


<dt>timer0</dt>
<dd>initial timer value from <code>proc.time()[3]</code></dd>


<dt>nsims</dt>
<dd>total number of simulations</dd>


<dt>nprints</dt>
<dd>total number of printed updates</dd>

</dl>

computeTimeRemaining: Estimate the remaining time in the algorithm

Description

Usage

Value

Arguments