Introduction to Bayes
Follow-up
- On Mattias’ website you can find links to the slides, interactive visualizations, and extra material, as well as a draft for Mattias’ book Bayesian Learning
- Mattias also uploaded exercises with solutions which build on the course contents
The day starts at 09:00 and ends at 16:00, with a break for lunch 12:00-13:00 (not provided). Coffee will be provided in the morning and afternoon.
See the schedule below and find more detailed information at the course page hosted by Mattias. There you can also find links to the slides, interactive visualizations, and extra material, as well as a draft for Mattias’ book Bayesian Learning.
In this one-day crash course, Mattias will introduce: the basics of Bayesian statistics though simple models, the Bayesian approach to prediction and decision making, approximation and sampling algorithms for posterior inference, Bayesian regression and classification, and probabilistic programming in Stan and Turing that allow the user to tackle serious real-world problems with ease.
Mattias Villani is a Professor of Statistics at Stockholm University, whose work focuses on developing models and computational methods for Bayesian prediction and decision making.
Anyone is welcome and no formal registration is needed.
Schedule
| Time | Topic |
|---|---|
| 09.00-09.50 | Lecture 1 The Bayesics |
| 10.00-10.50 | Lecture 2 Multi-parameter models, Marginalization, Prior elicitation and Prediction |
| 10.50-11.10 | ☕ Coffee |
| 11.10-12.00 | Lecture 3 Bayesian Regression and Regularization |
| 12.00-13.00 | 🍲 Lunch |
| 13.00-13.50 | Lecture 4 Bayesian Classification and Posterior Approximation |
| 14.00-14.50 | Lecture 5 Introduction to Gibbs sampling, MCMC and HMC |
| 14.50-15.10 | ☕ Coffee |
| 15.10-16.00 | Lecture 6 Implementing Bayesian Learning with Probabilistic Programming |