Wiki page, DAT465 Causality and causal inference: 1. Probability & graphical models

Wiki page
2021-08-23T10:16:53+02:00

Module contents

In this lecture, we cover

Short recap of probability

Directed probabilistic graphical models (Bayes networks)

Slides:

Slides [2021], Follow-up slides [2021]

Part 1: Probability (probability spaces, random variables, moments)

Part 2: Probabilistic dependence, directed graphical models (Bayes networks)

Corrections:

On Slide 62 in the recording, the title was wrong. It has been corrected in the slides above.

All slides are subject to change until the lecture is given.

Reading / watching suggestions

Mohri et al., Foundations of Machine Learning, Appendix C

Pearl, d-separation without tears

Koller & Friedman, Probabilistic Graphical Models: Principles and Techniques, Chapter 3 and course slides

Pearl, Causality (2nd ed.), Chapters 1.1–1.2

Brady Neal's videos on Graphical models

Previous year's course

In 2020, this course was given as a PhD course. The video for this module from that run is available below, and the slides are available here: [introduction, follow-up]. Make note that this does not completely reflect the content of this year's course.