This course wraps up the series of methods courses. We look at modelling from a birds-eye view, introducing advanced concepts and revisiting methods introduced in earlier courses from a comprehensive Bayesian perspective. We introduce causal reasoning using directed acyclic graphs (DAGs), mixture models, Gaussian processes, learn to deal with measurement error and missing data; and we revisit regression modelling, generalized linear models, multilevel modelling, Markov chain Monte Carlo sampling, learning to implement them using probabilistic programming.
The course is based on the textbook Statistical Rethinking (2nd edition, 2020) by Richard McElreath1. Please get a copy. The book's homepage contains lots of additional resources. In particular, please install the R package rethinking. Slides and recordings of the author's current course and of a previous version from 2019 are also available.
1McElreath, R. (2020). Statistical Rethinking: A Bayesian Course with Examples in R and Stan (2nd ed.). Chapman and Hall/CRC. doi:10.1201/9780429029608
| Course week | Week of year | Topics and readings |
|---|---|---|
| 1 | 5 | Statistical models (chapters 1,2) |
| 2 | 6 | Distributions and sampling (chapters 2,3) |
| 3 | 7 | Gaussian models and linear regression (chapter 4) |
| 4 | 8 | Several predictors, directed acyclic graphs (chapters 5) |
| 5 | 9 | Causal inference (chapter 6) |
| 6 | 10 | Model comparison (chapter 7) |
| 7 | 12 | Interactions (chapter 8) |
| 8 | 13 | Markov chain Monte Carlo, maximum entropy (chapters 9, 10) |
| 9 | 14 | Generalized linear models (chapters 11) |
| 10 | 17 | Mixture models, ordered categorical outcomes/predictors (chapter 12) |
| 11 | 18 | Multilevel models (chapter 13) |
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Portfolio consisting of 3 assignments
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Each assignment will require you to create an R Markdown notebook consisting of a mix of text and code.
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Due
- End of week 9 (Sunday 6 March, 23:59)
- End of week 13 (Sunday 3 April, 23:59)
- End of week 17 (Sunday 1 May, 23:59)
You will receive a (short) feedback message from us on your portfolio assignments that you can use for improvements before finalizing your hand-ins.