Probabilistic Reasoning

Literature and slides

Literature (mandatory)

Studymanual Probabilistic Reasoning [pdf], author: S. Renooij.
The studymanual describes in detail how the course is assessed, and the level of knowledge and understanding expected. The studymanual also contains questions to aid in your selfstudy.
Syllabus Probabilistic Reasoning with Bayesian networks [pdf], by L.C. van der Gaag, S. Renooij. (Possibly also available in paper from study association Sticky.)
Paper RR04: General state space Markov chains and MCMC algorithms by Gareth O. Roberts, Jeffrey S. Rosenthal (2004): read up to and including 3.2 thoroughly, and skim the rest.

Literature (optional)

Book BDA3: Bayesian Data Analysis (3rd edition; 2021) by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, Donald Rubin: Ch 1, up to and including 1.7.
Notes on Probabilistic Models: nice introduction to Bayesian parameter estimation, implicitly showing that inference (be it exact or approximate) and parameter learning are very similar tasks. Probabilistic models can be learned from data; in the syllabus we focus on structure learning for PGMs; in probabilistic programming you specify the 'structure' and learn only the parameters from data.

Course slides (mandatory)

The lectures cover most of the above-mentioned literature, but some subjects will be discussed in more detail. You should be familiar with the additional details covered in the course slides:

Part 1 (syllabus chapters):

Part 2 (RR04; Probabilistic programming):

these slides can be found on Blackboard (in Course Content)

Part 3 (syllabus chapters):

In case (some) slides are not yet available, you can check out last year's slides.

Video Lectures (optional)

🎦 For the 2020 online edition of the course the story that goes with the slides was recorded in videoclips. Each clip has its own subject and lasts about 10-30 minutes. These 'V-lectures' can be found on the UU video platform. Note that course slides have changed since 2020 and that the topic of probabilistic programming is not covered at all in the videos.

Further viewing: optional slides, videos etc

online courses, tutorials and background info:

Buffalo Probabilistic Graphical Models course;
The online (Coursera) Stanford Probabilistic Graphical Models courses;
The Helsinki B-Course;
video lectures, search on e.g. videolectures.net, youtube.com, see e.g. various videos on Graphical models and d-separation.