BayesCog: Bayesian Statistics and Hierarchical Bayesian Modeling for Psychological Science (200077-1 SE)
Advanced Seminar: Mind and Brain; 2020S
Instructor: Dr. Lei Zhang
Location: [virtually via Zoom]
When: 09:45-11:15 Wednesdays (see calendar below)
Recording: available on YouTube
See also a Twitter thread on the summary of the course.
L01: 18.03 Introduction and overview
L02: 27.03 Introduction to R
L03: 01.04 Probability; Bayes' theorem
L04: 22.04 Bayesian inference; Binomial model
L05: 29.04 Binomial model in Stan; Simple linear regression
L06: 06.05 Intro to cognitive modeling
L07: 13.05 Reinforcement Learning (concepts, meaning of parameters)
L08: 20.05 Implementing simple RL model
L09: 27.05 Hierarchical modeling
L10: 03.06 Stan optimization
L11: 10.06 PRL task and model comparison
L12: 17.06 Stan tips + Programming project
L13: 24.06 Programming project + Summary
| 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 |
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