We’ll be holding a virtual training workshop on NIMBLE, January 4-6, 2023 from 8 am to 1 pm US Pacific (California) time each day. NIMBLE is a system for building and sharing analysis methods for statistical models, especially for hierarchical models and computationally-intensive methods (such as MCMC and SMC).

Recently we added support for automatic differentiation (AD) to NIMBLE in a beta release, and the workshop will cover NIMBLE’s AD capabilities in detail.

The workshop will cover the following material:

• the basic concepts and workflows for using NIMBLE and converting BUGS or JAGS models to work in NIMBLE.
• overview of different MCMC sampling strategies and how to use them in NIMBLE, including Hamiltonian Monte Carlo (HMC).
• writing new distributions and functions for more flexible modeling and more efficient computation.
• tips and tricks for improving computational efficiency.
• using advanced model components, including Bayesian non-parametric distributions (based on Dirichlet process priors), conditional auto-regressive (CAR) models for spatially correlated random fields, Laplace approximation, and reversible jump samplers for variable selection.
• an introduction to programming new algorithms in NIMBLE.
• use of automatic differentiation (AD) in algorithms.
• calling R and compiled C++ code from compiled NIMBLE models or functions.

If you are interested in attending, please pre-register. Registration fees will be $125 (regular) or $50 (student).  We are also offering a process (see the pre-registration form) for students to request a fee waiver.

The workshop will assume attendees have a basic understanding of hierarchical/Bayesian models and MCMC, the BUGS (or JAGS) model language, and some familiarity with R.