INF2D: 23: Exact Inference in Bayesian Networks

Having shown how Bayesian Networks (BNs) are a compact representation of a joint probability distribution, we now present algorithms for using BNs to answer any probabilistic query.  This folder concentrates on algorithms for doing exact inference: in other words, the answer the algorithm provides is absolutely correct, rather than an approximation.  We'll study algorithms for approximate inference later in the course.

Lecture Slides

Lecture Slides (Last Year's Notes Version)

Required Reading

R&N Section 14.4  or NIE Chapter (14) "Probabilistic Reasoning", Section 4.

NOTE: The abbreviation R&N refers to:

“Artificial Intelligence: A Modern Approach” Third Edition, Russell R & Norvig P, Prentice Hall, 2010 (R&N).

The abbreviation NIE stands for the following edition of the same book:

“Artificial Intelligence: A Modern Approach” Third Edition, Pearson New International Edition, Russell R & Norvig P, Pearson, 2014.

Quiz 24: Exact Inference in 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.

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