# 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: [FGME 2017](http://www.fgme2017.de) will take place in Tuebingen near the city center in the buildings of the Psychology department (Schleichstraße 4).
### 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. Carry out sensitivity analyses to investigate how posteriors change as a result of prior specification.
6. Visualize and interpret different models.
7. Carry out posterior predictive checks and cross-validation for model evaluation.
### Class schedule, slides, and lecture notes
This one-day workshop will involve lectures interspersed with short exercises to be done in class.
+ 9-10:30 Session 1: Introduction to Bayesian data analysis, sampling algorithms
+ 10:30-11 Coffee break
+ 11-12:30 Session 2: Introduction to Stan
+ 12:30-14 Coffee break
+ 14-15:30 Session 3: Hierarchical models 1
+ 15:30-16 Coffee break
+ 16-17 Session 4: Hierarchical models 2
Slides, lecture notes and code + data will be provided on the day of the workshop and posted here as well.
### 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 instructors 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)