modelInitialization: Information on initial values in a NIMBLE model

In nimble: MCMC, Particle Filtering, and Programmable Hierarchical Modeling

Information on initial values in a NIMBLE model

Description

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:

• MCMC will auto-initialize but will do so from the prior distribution. This can cause slow convergence, especially in the case of diffuse priors.

• Likewise, particle filtering methods will initialize top-level parameters from their prior distributions, which can lead to errors or poor performance in these methods.

Please see this Section (https://r-nimble.org/html_manual/cha-mcmc.html#sec:initMCMC) of the NIMBLE user manual for further suggestions.

nimble documentation built on July 9, 2023, 5:24 p.m.