computeTimeRemaining

Estimate the remaining time in the MCMC based on previous samples

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

computeTimeRemaining function

<dl><dt>nsi</dt>
<dd>Current iteration</dd>
<dt>timer0</dt>
<dd>Initial timer value, returned from <code>proc.time()[3]</code></dd>
<dt>nsims</dt>
<dd>Total number of simulations</dd>
<dt>nrep</dt>
<dd>Print the estimated time remaining every <code>nrep</code> iterations</dd></dl>

Arguments

Estimate the remaining time in the MCMC based on previous samples — computeTimeRemaining

<dl>

<dt>nsi</dt>
<dd>Current iteration</dd>


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


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


<dt>nrep</dt>
<dd>Print the estimated time remaining every <code>nrep</code> iterations</dd>

</dl>

computeTimeRemaining: Estimate the remaining time in the MCMC based on previous samples

Description

Usage

Value

Arguments