CS3710  Probabilistic graphical models: Readings.

Instructor:  Milos Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos_at_cs.pitt.edu

Covered topics

Graphical models: basics

  • E. Charniak Bayesian networks without fears.
  • Kevin Murphy. An Introduction to Graphical models.
  • Michael I. Jordan Graphical models. Statistical Science (Special Issue on Bayesian Statistics), 19, 140-155, 2004.

    Independences in graphical models

    Inferences: complexities

    Markov Random Fields

    Variable elimination

    Pearl's algorithm

    Joint tree algorithm

    Recursive decomposition

    Local probability models and inferences with such models

    Monte Carlo approximations

    Adaptive importance sampling

    Bethe and Kikuchi Approximations

    Mean field /variational approximations

    Learning graphical models

    Expectation maximization

    Structural EM