Material for the course "Bayesian Data Analysis & Cognitive Modeling" held at the University of Tübingen during the spring term of 2017
The course introduces main ideas and tools of Bayesian data analysis. We will compare standard and Bayesian approaches to statistical inference. We will also look at Bayesian inference and model comparison for special-purpose cognitive models (with a focus on general cognition and psycholinguistics).
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| n | date | topic | reading (main) | extra info |
|---|---|---|---|---|
| 1 | 25/4 | overview & formalities | ||
| 2 | 28/4 | handling & plotting data in R | R for Data Science 3, 5, 12, 18, 21 | |
| 3 | 2/5 | primer on probability & “classical” statistics | Kruschke ch. 4 | |
| 4 | 5/5 | p-problems & Rmarkdown | Wagenmakers (2007), R for Data Science IV | Brechtbau 0.35 |
| 5 | 9/5 | intro to BDA | Krushke ch. 5 & 6 | |
| 6 | 12/5 | MCMC sampling | Kruschke ch. 7 | HW1 due |
| 7 | 16/5 | JAGS | Kruschke ch. 8 | |
| 8 | 19/5 | practice: parameter inference 1 | Lee & Wagenmakers ch. 3, 4 | |
| 9 | 23/5 | hierarchical modeling | Kruschke ch. 9 | |
| 10 | 26/5 | practice: parameter inference 2 | Lee & Wagenmakers ch. 5, 6 | HW2 due |
| 11 | 30/5 | theory: model comparison | Kruschke ch 10, Lee & Wagenmakers ch. 7 | |
| 12 | 2/6 | practice: model comparison | Lee & Wagenmakers ch. 7, 8 | |
| – | – | pentecoast | – | |
| 13 | 13/6 | computing Bayes factors | – | |
| 14 | 16/6 | Bayes in philosophy of science | – | HW3 due |
| – | 20/6 | no class | ||
| 15 | 23/6 | computing Bayes factors 2 | – | |
| 16 | 27/6 | estimation, comparison & criticism | Kruschke 11, 12 | |
| 17 | 30/6 | practice: Generalized Context Model | Lee & Wagenmakers ch. 17 | HW4 due |
| 18 | 4/7 | Stan | Kruschke ch. 14, Stan manual | |
| 19 | 7/7 | practice: cognitive models 2 | Lee & Wagenmakers ch. 11, | |
| 20 | 11/7 | generalized linear model | Kruschke ch 15, 16, 17 | |
| 21 | 14/7 | more on the GLM | Kruschke ch 16-22 | |
| 22 | 18/7 | mixed models & LOO | Sorensen et al. (2016) | |
| 23 | 21/7 | rounding off, project topics | ||
| 28/7 | HW5 due |