CS408: Advanced Artificial Intelligence

3.2: Probabilistic Methods for Uncertain Reasoning

• 3.2.1: Bayesian Network

  • Paulo C.G. Costa and Kathryn B. Laskey's "Bayesian Networks"

    Read this article on Bayesian networks. Focus on the definitions it provides and work through the example provided.
    

  • Christopher Bishop's "Introduction to Bayesian Inference" URL

    Watch this lecture, which discusses Bayesian Inference. You may wish to work through the slides provided on the right-hand side of the screen as Bishop lectures. Focus on learning the rules of probability and understanding the terms Bayes' theorem, Bayesian inference, probabilistic graphical models. Make sure you know how factor graphs are used.
    

• 3.2.2: Hidden Markov Model

  • Wikipedia: "Hidden Markov Model" URL

    Read this article, which discusses the hidden Markov model.
    

• 3.2.3: Other Methods for Uncertain Reasoning

  • 3.2.3.1: Kalman Filter

    • Wikipedia: "Kalman Filter" URL

      Read this article on the Kalman Filter.
      

  • 3.2.3.2: Decision Theory

    • Wikipedia: "Decision Theory" URL

      Read this article, making sure you understand the normative and descriptive decision theory and what kinds of decisions need a theory.