https://github.com/ccs-lab/aps2017-workshop

"Computational Modeling of Decision-Making Tasks With a Single Line of Coding: Modeling Can Be as Easy as Doing a T-Test"

https://github.com/ccs-lab/aps2017-workshop

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"Computational Modeling of Decision-Making Tasks With a Single Line of Coding: Modeling Can Be as Easy as Doing a T-Test"

https://github.com/CCS-Lab/APS2017-workshop/blob/master/

# APS2017-workshop
"Computational Modeling of Decision-Making Tasks With a Single Line of Coding: Modeling Can Be as Easy as Doing a T-Test"

http://www.psychologicalscience.org/conventions/annual/2017-workshops


The organizers (Young Ahn and Nate Haines at Ohio State University, https://ccs-lab.github.io/) of the workshop will post the outline of the workshop and detailed instructions along with R codes & slides for the workshop here. 




** **Please bring your own laptop with latest R (at least 3.3.2) and RStudio installed!**  ** 
We also recommend that participants install the [hBayesDM](https://github.com/CCS-Lab/hBayesDM) package prior to the workshop. Please click [here](http://rstudio-pubs-static.s3.amazonaws.com/164729_63b74e0329ff4d39aac2cfd0d8b21b5b.html#how-to-install-hbayesdm) for the instructions. **Mac users**, [make sure Xcode is installed](https://github.com/stan-dev/rstan/wiki/RStan-Mac-OS-X-Prerequisite-Installation-Instructions#step2_3). Xcode is 4.5GB in size, so it might be too big to download at the workshop site.




**Outline of the workshop** (May 28th (Sun), 2017)

Part I (by [Young Ahn](https://ccs-lab.github.io/team/young-ahn/)) (9:00am - 9:50am)
- What is computational modeling?
- How/why do we lower the barrier to computational modeling?
- Brief introduction to hBayesDM (**h**ierarchical **Bayes**ian modeling of **D**ecision-**M**aking tasks)
- How to fit a computational model?
  - Maximum likelihood estimation (MLE) 
  - Bayesian analysis & MCMC sampling
  - Hierarchical Bayesian analysis
  - Tools for Bayesian data analysis
- Things to know when performing MCMC sampling 




Part II (by [Nate Haines](https://ccs-lab.github.io/team/nate-haines/)) (10:00am - 10:50am)
- Hands-on tutorial on hBayesDM (data preparatation, model fitting, model comparisons, etc.)
- Goals: 
  - Learn to fit models in hBayesDM
  - Understand how to diagnose convergence issues when using Bayesian methods
  - Learn the differences between model comparison and parameter estimation
  - Have fun :D

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