Dr. Rina Dechter - University of California at Irvine ZOT!
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CompSci-276 Spring 2018, Reasoning in Graphical Models
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Course Project

  • The project information is available at: Project Information
  • The deadline for proposals is May 21st in class. Also send your proposals by email.

  • Course Reference

      Days: Monday/Wednesday
      Time: 11:00 am - 12:20 pm
      Room: ICS 180

      Instructor: Rina Dechter
      Office hours: Monday 4:00 pm - 5:00 pm

    Course Description

    The objective of this class is to provide an in-depth exposition of representation and reasoning under uncertainty using the framework of Graphical models. The class focuses on reasoning with uncertainty using directed and undirected graphical models such as Bayesian networks, Markov networks and constraint networks. These graphical models encode knowledge as probabilistic relations among variables. The primary reasoning tasks are, given some observations, to find the most likely scenario over a subset of propositions, or to update the degree of belief (distribution) over a subset of the variables. The primary algorithms (exact and approximate) using variational message-passing, search and sampling will be covered, with illustrations from areas such as bioinformatics, diagnosis and planning. Additional topics may include: causal networks, and dynamic decision networks (Influence diagrams and MDPS), as time permits.


    • 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 four sources:

    Additional sources:

    A longer list including secondary references.

    Some links to software and tools.


    Week       Date Topic Readings           Slides        
    Week 1 4/2
    • Introduction: Constraint and probabilistic graphical models
    Dechter1 ch. 1-2
    slides 1
    • The constraint network model: Graphs, modeling, Inference
    slides 2
    hw 1
    Week 2 4/9
    • Inference in constraints: Adaptive consistency, constraint propagation, arc-conistency
    • Lecture 3
    Dechter1 ch. 2-3
    Dechter2 ch. 3-4
    • Graph properties: induced-width, tree-width, chordal graphs, hypertrees, join-trees
    • Lecture 4
    Week 3 4/16
    • Bayesian and Markov networks: Representing independencies by graphs
    • Lecture 5
    slides 3
    hw 2
    Week 4 4/23
    • Probabilistic Inference: Bucket-elimination (summation and optimization)
    • Lecture 7
    Dechter1 ch.4-5 Slides 4

    • Probabilistic Inference: Tree-decompositions, Join-tree/Junction-tree algorithm
    • Lecture 8

    Week 5 4/30
    • Search: Backtracking search in CSPs; pruning by constraint propagation,
      backjumping and learning
    • Lecture 9

    Slides 5

    • Search: AND/OR search Spaces for likelihood, optimization queries
      (Probability of evidence, Partition function, MAP and MPE queries)
    • Lecture 10
    Dechter1 ch.6 hw 3
    Week 6 5/7

    • Bounded Inference: weighted Mini-bucket, belief-propagation,
      generalized belief propagation, variational, cost-shifting methods)
    • Lecture 12
    Dechter1 ch.8-9 Slides 6
    Week 7 5/14
    • Sampling: Gibbs sampling, Importance sampling, cutset-sampling,
      SampleSearch and AND/OR sampling, Stochastic Local Search
    • Lecture 13

    Slides 7

    5/16 hw 4
    Week 8 5/21
    • Mixed queries: (Influence diagrams, marginal map, mixture of constraints and probabilities)
    • Lecture 15

    • No class

    Week 9 5/28
    • Memorial Day - No class

    Slides 8
    Week 10 6/4

    • Project presentations

    Week 11 6/11
    • Project presentations


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

    Grading Policy:

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