registration open for online NIMBLE short course, June 3-5, 2020

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 open for NIMBLE short course, June 3-4, 2020 at UC Berkeley

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.

NIMBLE short course, June 3-4, 2020 at UC Berkeley

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.

NIMBLE short course at Bayes Comp 2020 conference

We’ll be giving a short course on NIMBLE on January 7, 2020 at the Bayes Comp 2020 conference being held January 7-10 in Gainesville, Florida, USA.

Bayes Comp is a popular biennial ISBA-sponsored conference focused on computational methods/algorithms/technologies for Bayesian inference.

The short course focuses on programming algorithms in NIMBLE and is titled:

“Developing, modifying, and sharing Bayesian algorithms (MCMC samplers, SMC, and more) using the NIMBLE platform in R.”

More details on the conference and the short course are available at the conference website.

Two day workshop: Flexible programming of MCMC and other methods for hierarchical and Bayesian models

We’ll be giving a two day workshop at the 43rd Annual Summer Institute of Applied Statistics at Brigham Young University (BYU) in Utah, June 19-20, 2018.

Abstract is below, and registration and logistics information can be found here.

This workshop provides a hands-on introduction to using, programming, and sharing Bayesian and hierarchical modeling algorithms using NIMBLE (r-nimble.org). In addition to learning the NIMBLE system, users will develop hands-on experience with various computational methods. NIMBLE is an R-based system that allows one to fit models specified using BUGS/JAGS syntax but with much more flexibility in defining the statistical model and the algorithm to be used on the model. Users operate from within R, but NIMBLE generates C++ code for models and algorithms for fast computation. I will open with an overview of creating a hierarchical model and fitting the model using a basic MCMC, similarly to how one can use WinBUGS, JAGS, and Stan. I will then discuss how NIMBLE allows the user to modify the MCMC – changing samplers and specifying blocking of parameters. Next I will show how to extend the BUGS syntax with user-defined distributions and functions that provide flexibility in specifying a statistical model of interest. With this background we can then explore the NIMBLE programming system, which allows one to write new algorithms not already provided by NIMBLE, including new MCMC samplers, using a subset of the R language. I will then provide examples of non-MCMC algorithms that have been programmed in NIMBLE and how algorithms can be combined together, using the example of a particle filter embedded within an MCMC. We will see new functionality in NIMBLE that allows one to fit Bayesian nonparametric models and spatial models. I will close with a discussion of how NIMBLE enables sharing of new methods and reproducibility of research. The workshop will include a number of breakout periods for participants to use and program MCMC and other methods, either on example problems or problems provided by participants. In addition, participants will see NIMBLE’s flexibility in action in several real problems.