Dr. Rina Dechter - University of California at Irvine ZOT!
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CompSci-276 Winter 2020, Reasoning in Graphical Models
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Course Project

  • The project information is available at: Project Information
  • The deadline for proposals is February 19th.

Course Reference

    Instructor:  Rina Dechter
    Days:  Monday/Wednesday
    Time:  11:00 am - 12:20 pm
    Room:  DBH 1300
    Office hours:  Thursdays, 12-1pm

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.


Week       Date Topic Readings           Slides/HW        
Week 1 1/6
  • Introduction and Background.
(a) Pearl 1-2
(b) Darwiche 1-3
(c) Russell-Norvig 13
(d) Darwiche.
Bayesian Networks
Slides 1
  • Bayesian and Markov networks: Representing Independencies by graphs.
HW 1
Week 2 1/13
Slides 2
  • Building Bayesian networks.
Darwiche Ch. 5 Slides 3
Week 3 1/20
    >>> No Class: MLK Holiday <<<
  • Probabilistic Inference: Bucket-elimination (summation, optimization).
Slides 4a
HW 2
Week 4 1/27
  • Local structures CPTs and Induced width algorithms.

Slides 4b

  • Probabilistic Inference: Tree-decompositions:
    Join-tree/Junction-tree algorithm. Cluster tree elimination.
Dechter Ch. 4,
Darwiche Ch. 6
Week 5 2/3
Dechter Ch. 5, 7.1,
Darwiche Ch. 7-8
Slides 5
HW 3
  • Probabilistic Inference by search: AND/OR search spaces.
Dechter Ch. 6-7
Week 6 2/10
    >>> No Class <<<

slides 6
HW 4
Week 7 2/17
    >>> No Class: President's Day <<<

  • Learning graphical models.
Darwiche Ch. 17
Slides 7
Week 8 2/24
  • Approximate algorithms by Bounded Inference.
Dechter Ch. 8-9
Darwiche Ch. 14
Slides 8
HW 5

Week 9 3/2
  • Approximate Algorithms by Sampling: MCMC schemes.
Darwiche Ch. 15
Paper: Cutset-Sampling
Slides 9b


Slides 9b
Week 10 3/9

  • Project presentations

  • Project presentations


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

Grading Policy:

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