INF2D: 23: Introduction to Bayesian Networks

This folder provides an introduction to Bayesian Networks, which is a compact way of representing a joint probability distribution.  In later sections, we'll see that Bayesian Networks are useful for providing a practical approach to probabilistic inference.  This folder consists of:

• Three videos of short lectures.  They cover:
  1. Introduction to Bayesian Networks
  2. Semantics of Bayesian Networks
  3. Efficient representations of conditional probability tables in Bayesian Networks
• Some required reading from Russell and Norvig.
• A quiz that tests your understanding of the material presented here.

Please watch the videos or attend the in-persion lecture, do the required reading, and attempt the quiz.  If there is anything you don't understand, then please ask your question at the lecture or post it on piazza.

Lecture 23 Slides: Whole

23.pdf

Required Reading

Quiz 23: Probabilistic Reasoning with Bayesian Networks

These questions are designed to test your understanding of the above course content; doing this quiz does not contribute to your overall grade.  Some questions require a text answer.  You can ask for formative feedback on these from your tutor or on piazza.  Other questions are multiple choice or they require a numeric answer: you will get immediate feedback for these. Please don't attempt this quiz until you have acquainted yourself with this lecture and the required reading.

You must be logged onto Learn to do this quiz.

Videos Recorded by Prof Alex Lascarides

23a: Introducation to Bayesian Networks

23a slides: 23a.pdf
23a video:

23b: The semantics of Bayesian Networks

23b slides: 23b.pdf
23b video:

23c: Efficient representations of conditional probability tables in Bayesian Networks

23c slides: 23c.pdf 
23c video: