Dr. Rina Dechter - University of California at Irvine ZOT!
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CompSci-276 Fall 2021, Reasoning in Graphical Models
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Course Reference

IMPORTANT NOTE: Some classes will be taught purely virtually via Zoom, while others will be taught in person. Please refer to the course calendar at the end of the page for updates on whether classes will be taught in person or virtually via Zoom.

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.

Course material

The course will be based mostly on three sources:

Additional sources:

A longer list including secondary references.

Some links to software and tools.

Course Topics

  • Introduction: probabilistic graphical models.
  • Bayesian and Markov networks: Representing independencies by graphs.
  • Building Bayesian networks.
  • Inference in Probabilistic models: Bucket-elimination (summation and optimization), Tree-decompositions, Join-tree/Junction-tree algorithm.
  • Graph properties: induced-width, tree-width, chordal graphs, hypertrees, join-trees.
  • Search in Graphical models: AND/OR search Spaces for likelihood, optimization queries.
  • Learning graphical models.
  • Approximate Bounded Inference: weighted Mini-bucket, belief-propagation, generalized belief propagation.
  • Approximation by Sampling: MCMC schemes, Gibbs sampling, Importance sampling.
  • Causal graphical models.


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

Grading Policy:

Homework (70%), class project (30%)

Course Project

(You will need to be logged into Google with you UCI account to access.)
Project Information Page
Project Sign-Up Sheet


Week Topic Lectures
Week 1

  • Introduction and Background.

  • Bayesian and Markov networks: Representing Independencies by graphs.

Lec 1

Lec 2

Slides 1

Slides 2

(a) Pearl ch. 1-2
(b) Darwiche ch. 1,3
(c) Russell-Norvig ch. 13
(d) Bayesian Networks

[Bayesian Networks]
(a) Pearl ch. 3
(b) Darwiche ch. 4

M 9/27

W 9/29
Week 2

  • d-seperation in DAGs

  • Building Bayesian networks.

Lec 3

Lec 4

Slides 3

Slides 4

HW 1

(a) Pearl ch. 3
(b) Darwiche ch. 4
(c) Darwiche ch. 5

M 10/4

W 10/6

Week 3

  • Probabilistic Inference: Bucket-elimination (summation, optimization)
  • Local structures CPTs and Induced width algorithms.

Lec 5 Rec Unavail.

Lec 6

Slides 5

M 10/11

W 10/13
Week 4

  • Probabilistic Inference: Tree-decompositions:
    Join-tree/Junction-tree algorithm. Cluster tree elimination.

Lec 7

Lec 8

Slides 6

HW 2

Dechter Ch. 4,
Darwiche Ch. 6
Dechter Ch. 5, 7.1,
Darwiche Ch. 7-8

M 10/18

W 10/20

Week 5

  • Probabilistic Inference by search: AND/OR search spaces

Lec 9

Lec 10

Slides 7

HW 3

Dechter Ch. 6-7

M 10/25

W 10/27

Week 6

  • Learning graphical models.

Lec 11

Lec 12

Slides 8

HW 4

Darwiche Ch. 17

M 11/1

W 11/3

Week 7

  • Approximate algorithms by Bounded Inference.

Lec 13

Lec 14

Slides 9

Darwiche Ch. 14
Dechter Ch. 8-9

M 11/8

W 11/10

Week 8

  • Approximate Algorithms by Sampling: MCMC schemes.

Lec 15

Lec 16

Slides 10(a)

Slides 10(b)

Darwiche Ch. 15
Paper: Cutset-Sampling

M 11/15

W 11/17

Week 9

  • Approximate Algorithms by Sampling: MCMC schemes (cont.)

Lec 17

HW 5

M 11/22

W 11/24

Week 10

  • Project presentations

Lec P1

Lec P2

M 11/29

W 12/1

Finals Week

  • Project presentations

Lec P3

F 12/10