This folder presents several alternative methods for doing approximate inference in Bayesian Networks. It consists of:
- two videos of short lectures. They cover:
- Direct sampling methods
- Monte Carlo sampling methods
- 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 25 Slides: Whole!
25a: Approximate Inference in BNs: Direct Sampling Methods
25a slides: 25a.pdf
25a video:
25b: Approximate Inference in BNs: Monte Carlo Sampling Methods
25b slides: 25b.pdf
25b video:
Required Reading
R&N Section 14.5 or NIE Chapter (14) "Probabilistic Reasoning", Section 5.
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 25: Approximate Inference in Bayesian Networks
TThese 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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