The EnviBayes section has recently launched a series of monthly Zoom meetings.
The November meeting will be held November 18, 2021 at 11AM US Eastern (8AM US Pacific, 5PM Central European time) and it will feature a 2-hour NIMBLE tutorial with Chris Paciorek from UC Berkeley.
The 2-hour workshop is open to all ISBA members. To register for the tutorial, please use the following link:
https://uci.zoom.us/meeting/register/tJwkf-CvrzsoG9YNWKAJvkTzgYPUaYzPChef
Title and abstract are provided below.
Best,
Veronica
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Presenter: Chris Paciorek, UC Berkeley
Title: Programming with hierarchical statistical models: Using the flexible NIMBLE system for MCMC and more
Abstract:
NIMBLE (r-nimble.org) is a system for fitting and programming with hierarchical models in R that builds on (a new implementation of) the BUGS language for declaring models. NIMBLE provides analysts with a flexible system for using MCMC, sequential Monte Carlo, MCEM, and other techniques on user-specified models. It provides developers and methodologists with the ability to write algorithms in an R-like syntax that can be easily disseminated to users. C++ versions of models and algorithms are created for speed, but these are manipulated from R without any need for analysts or algorithm developers to program in C++. While analysts can use NIMBLE as a nearly drop-in replacement for WinBUGS or JAGS, NIMBLE provides enhanced functionality in a number of ways.
This workshop will demonstrate how one can use NIMBLE to:
– flexibly specify an MCMC for a specific model, including choosing samplers and blocking approaches (and noting the potential usefulness of this for teaching);
– tailor an MCMC to a specific model using user-defined distributions, user-defined functions, and vectorization;
– write your own MCMC sampling algorithms and use them in combination with samplers from NIMBLE’s library of samplers;
– develop and disseminate your own algorithms, building upon NIMBLE’s existing algorithms; and
– use specialized model components such as Dirichlet processes, conditional auto-regressive (CAR) models, and reversible jump for variable selection.