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.

We’ll be holding a in-person training workshop on NIMBLE, June 1-3, 2022, in Lisbon, Portugal, sponsored by the Centro de Estatística e Aplicações at the Universidade Lisboa (CEAUL).

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).

More details and registration are available at the workshop website. No previous NIMBLE experience is required, but the workshop will assume some familiarity with hierarchical models, Markov chain Monte Carlo (MCMC), and R.

We’ll be giving a two-hour tutorial on NIMBLE, sponsored by the environmental Bayes (enviBayes) section of ISBA (The International Society for Bayesian Analysis), on Thursday November 18, from 11 am to 1 pm US Eastern time.

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.

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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.

The tutorial will assume working knowledge of hierarchical models and some familiarity with MCMC. Given the two-hour time frame, we’ll focus on demonstrating some of the key features of NIMBLE, without going into a lot of detail on any given topic.

To attend, please register here.

We’ll be holding a virtual training workshop on NIMBLE, May 26-28, 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).

The workshop will roughly follow the material covered in our June 2020 virtual training, in particular:

• 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.
• 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, and reversible jump samplers for variable selection.
• an introduction to programming new algorithms in NIMBLE.
• calling R and compiled C++ code from compiled NIMBLE models or functions.

If participant interests vary sufficiently, the third session will be split into two tracks. One of these will likely focus on ecological models. The other will be chosen based on attendee interest from topics such as (a) advanced NIMBLE programming including writing new MCMC samplers, (b) advanced spatial or Bayesian non-parametric modeling, or (c) non-MCMC algorithms in NIMBLE, such as sequential Monte Carlo. 

If you are interested in attending, please pre-register at https://forms.gle/6AtNgfdUdvhni32Q6. This will hold a spot for you and allow us to learn about your specific interests. No payment is necessary to pre-register. Fees to finalize registration will be $100 (regular) or $50 (student).  We will offer a process 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.

Registration is now open for a three-day online training workshop on NIMBLE, June 3-5, 2020, 9 am – 2 pm California time (noon – 5 pm US EDT). This online workshop will be held in place of our previously planned in-person workshop.

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).

The workshop will cover:

• 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.
• 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, and reversible jump samplers for variable selection.
• an introduction to programming new algorithms in NIMBLE.
• calling R and compiled C++ code from compiled NIMBLE models or functions.

If participant interests vary sufficiently, part of the third day will be split into two tracks. One of these will likely focus on ecological models. The other will be chosen based on attendee interest from topics such as (a) advanced NIMBLE programming including writing new MCMC samplers, (b) advanced spatial or Bayesian non-parametric modeling, or (c) non-MCMC algorithms in NIMBLE such as sequential Monte Carlo. Prior to the workshop, we will survey attendee interests and adjust content to meet attendee interests.

To register, please go here: https://na.eventscloud.com/540175. Registration is $120 (regular) or $60 (grad student).

Registration is now open for a two-day training workshop on NIMBLE, June 3-4, 2020 in Berkeley, California. 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).

The workshop will cover:

• 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.
• 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, and reversible jump samplers for variable selection.
• an introduction to programming new algorithms in NIMBLE.
• calling R and compiled C++ code from compiled NIMBLE models or functions.

If participant interests vary sufficiently, the second half-day will be split into two tracks. One of these will likely focus on ecological models. The other will be chosen based on attendee interest from topics such as (a) advanced NIMBLE programming including writing new MCMC samplers, (b) advanced spatial or Bayesian non-parametric modeling, or (c) non-MCMC algorithms in NIMBLE such as sequential Monte Carlo. Prior to the workshop, we will survey attendee interests and adjust content to meet attendee interests.

To register, please go here: https://na.eventscloud.com/528790. Registration is $230 (regular) or $115 (student).

We have also reserved a block of rooms on campus for $137 per night, including breakfast and lunch. To reserve a room, go here: https://na.eventscloud.com/528792.

We’ll be holding a two-day training workshop on NIMBLE, June 3-4, 2020 in Berkeley, California. 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).

The tutorial will cover

• 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.
• 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, and reversible jump samplers for variable selection.
• an introduction to programming new algorithms in NIMBLE.
• calling R and compiled C++ code from compiled NIMBLE models or functions.

If participant interests vary sufficiently, the second half-day will be split into two tracks. One of these will likely focus on ecological models. The other will be chosen based on attendee interest from topics such as (a) advanced NIMBLE programming including writing new MCMC samplers, (b) advanced spatial or Bayesian non-parametric modeling, or (c) non-MCMC algorithms in NIMBLE such as sequential Monte Carlo.  Prior to the workshop, we will survey attendee interests and adjust content to meet attendee interests.

If you are interested in attending, please pre-register to hold a spot at https://forms.gle/6AtNgfdUdvhni32Q6.  The form also asks if you are interested in a relatively cheap dormitory-style housing option.  No payment is necessary to pre-register. Fees to finalize registration will be $230 (regular) or $115 (student).  We hope to be able to offer student travel awards; more information will follow.

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.