# Reasoning with Bayesian Networks

This page contains information on the second half of
COMP8620 Advanced Topics in AI,
offered in Semester 2, 2008 (Phil Kilby discusses
search in the
first half).
In this part of the course we discuss
probabilistic reasoning with Bayesian networks,
with a focus on how problem structure can be exploited
in various ways to improve the efficiency and scalability of reasoning.
Textbook: *Modeling and Reasoning with Bayesian Networks*, Adnan Darwiche,
Cambridge University Press, 2009.

**Time:** Wed 10–12

**Place:** Graduate Teaching Room, R221, Ian Ross Building

**Instructor:** Jinbo Huang

### Schedule

- [Slides] Week 1: Probability calculus, Bayesian networks
- [Slides] Week 2: Building Bayesian networks, inference by variable elimination
- [Slides] Week 3: Inference by factor elimination, inference by conditioning
- [Slides] Week 4: Models for graph decomposition, most likely instantiations
- [Slides] Week 5: Complexity of probabilistic inference, compiling Bayesian networks
- [Slides] Week 6: Inference with local structure, selected applications

### Assignments

- Due Wed 15 Oct at beginning of lecture. Complete the following 8 exercises from the textbook: 3.21, 3.22, 4.11, 4.25, 5.1, 5.11, 6.1, 6.4. Late assignments will be penalized by 10% per day, ignoring weekends. Not accepted after beginning of lecture Wed 22 Oct.
- Due Wed 29 Oct at beginning of lecture. Complete the following 6 exercises from the textbook: 7.14, 8.9, 9.27, 10.8, 11.4, 12.3. Late assignments will be penalized by 10% per day, ignoring weekends. Not accepted after Wed 5 Nov.