# Introduction to Bayesian Modeling using Stan ## 17 September 2017 [Link to github repository](https://github.com/vasishth/FGME_Stan_2017) ### Overview In this one-day workshop, we will give a comprehensive introduction to using Stan for Bayesian data analysis and Bayesian modeling. We will provide lecture notes and suggested readings for further study. We assume that everyone has a laptop with them and has the R package [rstan](http://mc-stan.org/interfaces/rstan) installed within R. + Instructors: [Shravan Vasishth](http://www.ling.uni-potsdam.de/~vasishth/) and [Bruno Nicenboim](http://www.ling.uni-potsdam.de/~nicenboim/) + Workshop date: 17 September 2017 + Location: to be announced + Textbook: none ### Goals By the end of the course, participants should be able to: 1. Understand the fundamental ideas behind Bayesian data analysis. 2. Understand the essentials of Stan. 3. Fit standard models (such as linear models) in Stan. 4. Fit hierarchical models in Stan with different kinds of dependent variables. 5. Fit mixture models (including hierarchical mixtures). 6. Carry out sensitivity analyses to investigate how posteriors change as a result of prior specification. 7. Visualize and interpret different models. 8. Carry out posterior predictive checks and cross-validation for model evaluation. ### Class structure This one-day workshop will involve lectures interspersed with short exercises to be done in class. ### Final project In order to consolidate understanding, we will assign a project that participants can carry out (this is optional). Students have the option to submit it to the instructor a week later and get feedback. ### Resources + [Stan homepage](http://mc-stan.org) + [Clark's tutorial on Bayes](http://m-clark.github.io/docs/IntroBayes.html) + [Michael Franke's Bayesian modeling course at Tuebingen](http://www.sfs.uni-tuebingen.de/~mfranke/bda+cm2015/) + [Linear Modeling lecture notes, MSc Cognitive Systems, University of Potsdam](https://github.com/vasishth/LM) + [A tutorial on Bayesian Linear Mixed Models](http://www.ling.uni-potsdam.de/~vasishth/statistics/BayesLMMs.html) + [Statistical methods for linguistic research: Foundational Ideas – Part II](http://www.ling.uni-potsdam.de/~vasishth/pdfs/StatMethLingPart2.pdf) + [Repsychling package](https://github.com/dmbates/RePsychLing) + [Two statistics courses taught at European Summer School in Logic, Language, and Information, Barcelona, 2015](http://www.ling.uni-potsdam.de/~vasishth/statistics/ESSLLI2015Vasishth.html)