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(This article was first published on <strong><a href="https://r-nimble.org/two-day-workshop-flexible-programming-of-mcmc-and-other-methods-for-hierarchical-and-bayesian-models"> R – NIMBLE</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>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.</p>
<p>Abstract is below, and registration and logistics information can be found <a rel="nofollow" href="http://statistics.byu.edu/content/43rd-annual-summer-institute-applied-statistics" target="_blank">here</a>.</p>
<p>This workshop provides a hands-on introduction to using, programming, and sharing Bayesian and hierarchical modeling algorithms using NIMBLE (<a rel="nofollow" href="https://r-nimble.org/" target="_blank">r-nimble.org</a>). 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.</p>
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