modelInitialization

Having uninitialized nodes in a NIMBLE model can potentially cause some algorithms to fail and can lead to poor performance in others. Here are some
 general guidelines on how non-initialized variables can affect performance:<ul>
<li>MCMC will auto-initialize but will do so from the prior distribution. This can cause slow convergence, especially in the case of diffuse priors.</li>
<li>Likewise, particle filtering methods will initialize top-level parameters from their prior distributions, which can lead to errors or poor performance in these methods.</li>
</ul>Please see this Section (<a href="https://r-nimble.org/html_manual/cha-mcmc.html#sec:initMCMC">https://r-nimble.org/html_manual/cha-mcmc.html#sec:initMCMC</a>) of the NIMBLE user manual for further suggestions.

A system for writing hierarchical statistical models largely
compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models
and do basic R-style math, and compiling both models and nimbleFunctions via
custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace
Approximation, Monte Carlo Expectation Maximization, and some other tools.
The nimbleFunction system makes it easy to do things like implement new MCMC
samplers from R, customize the assignment of samplers to different parts of
a model from R, and compile the new samplers automatically via C++ alongside
the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language
by making it extensible: New distributions and functions can be added,
including as calls to external compiled code. Although most people think
of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models,
one can use 'NIMBLE' for writing arbitrary other kinds of model-generic
algorithms as well. A full User Manual is available at <https://r-nimble.org>.

Christopher Paciorek

nimble

MCMC, Particle Filtering, and Programmable Hierarchical Modeling

Perry de Valpine

Daniel Turek

Nick Michaud

Cliff Anderson-Bergman

Fritz Obermeyer

Claudia Wehrhahn Cortes

Abel Rodríguez

Duncan Temple Lang

Wei Zhang

Sally Paganin

Joshua Hug

Paul van Dam-Bates

Jagadish Babu

Lauren Ponisio

Peter Sujan

modelInitialization function

Having uninitialized nodes in a NIMBLE model can potentially cause some algorithms to fail and can lead to poor performance in others. Here are some
 general guidelines on how non-initialized variables can affect performance:<ul>
<li>MCMC will auto-initialize but will do so from the prior distribution. This can cause slow convergence, especially in the case of diffuse priors.</li>
<li>Likewise, particle filtering methods will initialize top-level parameters from their prior distributions, which can lead to errors or poor performance in these methods.</li>
</ul>Please see this Section (<a href='https://r-nimble.org/html_manual/cha-mcmc.html#sec:initMCMC'>https://r-nimble.org/html_manual/cha-mcmc.html#sec:initMCMC</a>) of the NIMBLE user manual for further suggestions.

Information on initial values in a NIMBLE model — modelInitialization

Information on initial values in a NIMBLE model

modelInitialization: Information on initial values in a NIMBLE model

Description

Arguments