Dr. Rina Dechter - University of California at Irvine ZOT!
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CMSC828X Spring 2019, Advanced Topics in Information Processing:
Algorithms for Probabilistic and Deterministic Graphical Models

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Course Project

Course Reference

    Days: Tuesday/Thursday
    Time: 3:30 pm - 4:45 pm
    Room: CSI 3120
    Instructor: Rina Dechter
    Office hours: Monday 11:30 am - 12:30 pm (3219 AVW)

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.


In this class I will teach the algorithmic principles that allow reasoning and learning for graphical models. Students will have 5 problems set of homework’s (50%) and a project (20%) that include presenting recent papers in the area. There will be a final (30%).


  • 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 Topics

  • Introduction: Constraint and probabilistic graphical models.
  • Inference in constraints: Adaptive consistency, constraint propagation, arc-conistency.
  • Graph properties: induced-width, tree-width, chordal graphs, hypertrees, join-trees.
  • 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.
  • Search in CSPs: Backtracking, pruning by constraint propagation, backjumping and learning.
  • Search in Graphical models: AND/OR search Spaces for likelihood, optimization queries.
  • Approximate Bounded Inference: weighted Mini-bucket, belief-propagation, generalized belief propagation.
  • Approximation by Sampling: MCMC schemes, Gibbs sampling, Importance sampling.
  • Causal Inference with causal graphs.


Week       Date Topic Readings / Resoures  Slides / HW    
Week 1 1/29
    (class canceled due to weather)
  • Introduction: Constraint and Probabilistic Graphical Models
Dechter1 ch. 1-2
Slides 1
Week 2 2/5
  • Constraint Network Model
Dechter1 ch. 3
Dechter2 ch. 2-3
Slides 2
  • Inference: Adaptive-Consistency

Dechter2 ch. 2
Montanari paper


Slides 3

Problem Set 1
Week 3 2/12
  • Graph Algorithms, Constraint Propagation
Slides 4
Constraint-Propagation (Bessiere)
Chapter3 - Constraints
Week 4 2/19
  • Bayesian and Markov networks: Representing independencies by graphs
Darwiche ch. 4
Slides 5

Problem Set 2
Week 5 2/26

  • Building Bayesian Networks
Darwiche ch. 5
Slides 6
Week 6 3/5
  • Probabilistic Inference: Bucket-elimination (Summation, Optimization)
Dechter1 ch. 4
Darwiche ch. 6

Slides 7a

Problem Set 3
Week 7 3/12
  • Probabilistic Inference: Tree-Decomposition (Bucket-Trees, Join-Trees)
Dechter1 ch. 5
Darwiche ch. 7,8
Slides 7b
Week 8 3/19
  • Spring break
  • Spring break
Week 9 3/26
  • Backtracking search for CSPs
Dechter2 ch. 5,6
Slides 8
Problem Set 4
Week 10 4/2
  • Search: AND/OR search Spaces for likelihood, optimization queries
                  (Probability of evidence, Partition function, MAP and MPE queries)
Dechter1 ch. 6
Slides 9
Week 11 4/9
  • Bounded Inference: weighted Mini-bucket, belief-propagation, generalized belief propagation, variational, cost-shifting methods)
chapter 8:Bounding Inference: Decomposition Bounds
Slides 10
chapter 9 Bounding Inference: Iterative Message-Passing
Week 12 4/16
  • Sampling: MCMC methods for graphical models: Importance sampling and Gibbs sampling schemes

Slides 11a

Problem Set 5
Darwiche ch. 15
Paper: Cutset-Sampling
Slides 11b
Week 13 4/23
    (no class)
  • Causal Graphical models
Slides 12
Week 14 4/30
  • Guest Speaker: Joshua Brule on Causal Programming
Week 15 5/7
Week 16 5/14
Week 17 5/22
  • Final Exam

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