Course n°03
Markov Chain Monte Carlo, generalised linear model
To be done ahead of this course
- Check previous courses.
- (optional) Read chapters 5, 10, and 11 from Statistical Rethinking.
Slides
Slides for this course are available below. You can navigate these sides using the ← and → keys. Pressing m will display all commands you can use (e.g., f to switch to full screen mode). You can also download a PDF version of these slides or the R code associated with these slides by clicking on the buttons below.
Tip
See all revealjs commands by pressing m then h.
To go further
- Betancourt, M. (2018). A Conceptual Introduction to Hamiltonian Monte Carlo (arXiv:1701.02434). arXiv. https://arxiv.org/abs/1701.02434
- Feng, C. (2022). Chi-feng/mcmc-demo [JavaScript]. https://github.com/chi-feng/mcmc-demo / https://chi-feng.github.io/mcmc-demo/
- Dogucu, A. A. J., Miles Q., & Ott, Mine. (2022). Chapter 7 - MCMC under the Hood. In Bayes Rules! An Introduction to Applied Bayesian Modeling. Available online at: https://www.bayesrulesbook.com/chapter-7.html.
- Nalborczyk, L. (2018). Using R to make sense of the generalised linear model. Blogpost available at https://lnalborczyk.github.io/post/glm/.