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ICS-275B Spring 2005, Network-Based Reasoning - Bayesian Networks
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Projects - pdf

Course Reference

  • Days: Monday/Wednesday
  • Time: 11:00 p.m. - 12:20 p.m.
  • Room: SE2 1304
  • Instructor: Rina Dechter

Course Description

One of the main challenges in building intelligent systems is the ability to reason under uncertainty, and one of the most successful approaches for dealing with this challenge is based on the framework of Bayesian networks, also called graphical models. Intelligent systems based on Bayesian networks are being used in a variety of real-world applications including diagnosis, sensor fusion, on-line help systems, credit assessment, bioinformatics and data mining.

The objective of this class is to provide an in-depth exposition of knowledge representation and reasoning under uncertainty using the framework of Bayesian networks.  Both theoretical underpinnings and practical considerations will be covered, with a special emphasis on dependency and independency models, on construction Bayesian graphical models and on exact and approximate probabilistic reasoning algorithms. Additional topics include: causal networks, learning Bayesian network parameters from data and dynamic Bayesian networks.


  • Familiarity with basic concepts of probability theory.
  • Knowledge of basic computer science, algorithms and programming principles.
  • Previous exposure to AI is desirable but not essential.

Tentative Syllabus

Week           Date Topic Readings Homeworks
Week 1 4/4
  • Introduction and background.
Pearl's book, chapters 1-2. Russell chapter 13. Koller chapter 3 (optional). Homework 1
  • Bayesian network representation: Independence properties.
Pearl chapter 3.  
Week 2 4/11
  • Bayesian network: Directed graphical models of independence.
  Homework 2
  • Markov network: Undirected graphical models of independence. Knowledge engineering.
Koller chapter 5.  
Week 3 4/18
  • Exact reasoning by inference: variable elimination.
Bucket Elimination. R. Dechter.
AAAI98 Tutorial on Reasoning.
Homework 3
  • Exact reasoning by inference: bucket trees, join-trees and polytrees propagation algorithms.
Pearl chapter 4.
A Note on Bucket Elimination. R. Dechter.
Exact inference (slides).
Week 4 4/25
  • Hybrids of search and inference schemes.
  • The reasoning tasks of MPE and MAP.
Pearl chapter 5. Homework 4
  • Exact reasoning by search: AND/OR search spaces.
Koller chapter 8.
AND/OR Search Spaces for Graphical Models. R. Dechter
Recursive Conditioning. A. Darwiche
Exact inference (slides).
Week 5 5/2
  • Approximate reasoning by sampling: MCMC methods (Gibbs sampling), importance sampling.
  • Rao-Blackwellised, custet conditioning sampling.
Sampling Bayesian Networks (slides)
Particle Filtering (slides)
AIS-BN. M. Druzdzel and J. Cheng
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. A. Doucet et al.
Homework 5
  • Discussion section.
Week 6 5/9
  • Approximate reasoning by bounded inference: mini-bucket and mini-clustering, Generalized belief propagation.
Bounded inference (slides). Homework 6
  • Canonical models; local structures.
On the impact of causal independence. I. Rish and R. Dechter.  
Week 7 5/16
  • Continuous and hybrid Bayesian networks.
Russell chapter 15.
Koller chapter 4.
Lauritzen paper.
Murphy paper.
Inference in Continuous and Hybrid Networks (slides).
Homework 7
  • Causal networks.
Pearl's book on causal reasoning.  
Week 8 5/23
  • Causal networks.
Pearl's book on causal reasoning.
Slides 1
  • Causal networks.
Pearl's book on causal reasoning.
Slides 2
Week 9 5/30
  • Memorial weekend.
  • Causal networks.
Pearl's book on causal reasoning. Homework 8
Week 10 6/6
  • Causal networks.
Pearl's book on causal reasoning.  
  • Project presentations.

Readings (partial list)


  • Judea Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1990.
  • K. B. Korb and A. E. Nicholson, Bayesian Artificial Intelligence,  Chapman and Hall/CRC, 2004
  • Finn V. Jensen. An introduction to Bayesian networks. UCL Press, 1996.
  • Russell and Norvig, Artificial intelligence, a modern approach (chapters, 13,14,15)
  • J. Pearl, Causality, Models, Reasoning and Inference, Cambridge University Press, 2000
  • Castillo, E.; Gutierrez, J.M.; Hadi, A.S., Expert Systems and Probabilistic Network Models, Springer-Verlag 1997
  • Robert G. Cowell, A. Philip Dawid, Steffen L. Lauritzen, David J. Spiegelhalter Probabilistic Networks and Expert Systems Springer-Verlag, 1999

Free Software

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There will be homework assignments and students will also be engaged in projects.

Grading Policy:

Homeworks and projects (50%), midterm (50%)