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Tuesday, June 28 • 11:06am - 11:24am
Bayesian analysis of generalized linear mixed models with JAGS

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BUGS is a language for describing hierarchical Bayesian models which syntactically resembles R. BUGS allows large complex models to be built from smaller components. JAGS is a BUGS interpreter written in C++ which enables Bayesian inference using Markov Chain Monte Carlo (MCMC). Several R packages provide interfaces to JAGS (e.g. jags, runjags, R2jags, bayesmix, iBUGS, jagsUI, HydeNet). The efficiency of MCMC depends heavily on the sampling methods used. Therefore a key function of the JAGS interpreter is to identify design motifs in a large complex Bayesian model that have well-characterized MCMC solutions and apply the appropriate sampling methods. Generalized linear models (GLMs) form a recurring design motif in many hierarchical Bayesian models. Several data augmentation schemes have been proposed that reduce a GLM to a linear model and allow efficient sampling of the coefficients. These schemes are implemented in the glm module of JAGS. The glm module also includes an interface to the sparse matrix algebra library CHOLMOD, allowing the analysis of GLMs with sparse design matrices. The use of sparse matrices in a Bayesian GLM renders the distinction between ``fixed'' and ``random'' effects irrelevant and allows all coefficients of a generalized linear mixed model to be sampled in the same comprehensive framework.

avatar for Ben Goodrich

Ben Goodrich

Lecturer in the Discipline of Political Science, Columbia University
Ben Goodrich is a core developer of Stan, which is a collection of statistical software for Bayesian estimation of models, and is the maintainer of the corresponding rstan and rstanarm R packages. He teaches in the political science department and in the Quantitative Methods in the Social Sciences master's program at Columbia University.

avatar for Martyn  Plummer

Martyn Plummer

International Agency for Research on Cancer
I am a member of the R Core Team and co-president of the R Foundation. My main methodological interest is in Bayesian inference and my applied work is in cancer epidemiology.

Attendees (97)