Teaching materials for BayesCog at Faculty of Psychology, University of Vienna
Bayesian Statistics and Hierarchical Bayesian Modeling for Psychological Science
Teaching materials for the award winning* BayesCog seminar at Faculty of Psychology, University of Vienna, as part of the Advanced Seminar for master's students (Mind and Brain track; recorded 2020/2021 Summer Semester).
Instructor: Dr. Lei Zhang
Location: [virtually via Zoom]
Recording: available on YouTube (also see below). The most recent recording from the 2021 summer semester is also available on Youtube.
Outreach: Twitter thread (being liked 700+ times on Twitter) summarizing the contents of the course.
Award/Recognition: * This course received a commendation award from the Society for the Improvement of Psychological Science (SIPS) (also see a tweet), as well as an ECR Teaching Award from the Faculty pf Psychology, University of Vienna.
L01: 18.03 Introduction and overview <slides> <video>
L02: 27.03 Introduction to R/RStudio I <slides> <video>
L03: 27.03 Introduction to R/RStudio II <slides> <video>
L04: 22.04 Probability and Bayes' theorem <slides> <video>
L05: 29.04 Linking data and parameter/model <slides> <video>
L06: 06.05 Grid approximation of Binomial model & intro to MCMC <slides> <video>
L07: 13.05 Intro to Stan I and Binomial model in Stan <slides> <video>
L08: 20.05 Intro to Stan II and Regression models in Stan <slides> <video>
L09: 27.05 Intro to cognitive modeling & Rescorla-Wagner model <slides> <video>
L10: 03.06 Implementing Rescorla-Wagner in Stan <slides> <video>
L11: 10.06 Hierarchical modeling + Stan optimization <slides> <video>
L12: 17.06 Model comparison <slides> <video>
L13: 24.06 Stan tips & debugging in Stan <slides> <video>
Folder | Task | Model |
---|---|---|
00.cheatsheet | NA | NA |
01.R_basics | NA | NA |
02.binomial_globe | Globe toss | Binomial Model |
03.bernoulli_coin | Coin flip | Bernoulli Model |
04.regression_height | Observed weight and height | Linear regression model |
05.regression_height_poly | Observed weight and height | Linear regression model |
06.reinforcement_learning | 2-armed bandit task | Simple reinforcement learning (RL) |
07.optm_rl | 2-armed bandit task | Simple reinforcement learning (RL) |
08.compare_models | Probabilistic reversal learning task | Simple and fictitious RL models |
09.debugging | Memory Retention | Exponential decay model |
Python
and Stan
.Stan
.[Journal articles]
- Kruschke, J. K., & Liddell, T. M. (2018). Bayesian data analysis for newcomers. Psychonomic bulletin & review, 25(1), 155-177.
- Wagenmakers, E. J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., ... & Matzke, D. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychonomic bulletin & review, 25(1), 35-57.
- Daw, N. D. (2011). Trial-by-trial data analysis using computational models. Decision making, affect, and learning: Attention and performance XXIII, 23, 3-38.
- Etz, A., Gronau, Q. F., Dablander, F., Edelsbrunner, P. A., & Baribault, B. (2018). How to become a Bayesian in eight easy steps: An annotated reading list. Psychonomic Bulletin & Review, 25(1), 219-234.
- Ahn, W. Y., Haines, N., & Zhang, L. (2017). Revealing neurocomputational mechanisms of reinforcement learning and decision-making with the hBayesDM package. Computational Psychiatry, 1, 24-57.
[Books]
- McElreath, R. (2020). Statistical Rethinking: A Bayesian Course with Examples in R and Stan, 2nd Ed. CRC Press.
- Lambert, B. (2018). A Student’s Guide to Bayesian Statistics. Sage.
For bug reports, please contact Lei Zhang ([email protected], or @lei_zhang_lz).
Thanks to Markdown Cheatsheet and shields.io.
This license (CC BY-NC 4.0) gives you the right to re-use and adapt, as long as you note any changes you made, and provide a link to the original source. Read here for more details.