KTH Mathematics  


A Graduate Course:
Bayesian Networks (7.5 p)
FSF 3970

This course is of interest for engineers, statisticians and computer scientists who work with,e.g., modelling of highly complex systems, signal processing, data mining, artificial intelligence, robotics, or need understanding of statistical models using probabilities factorized according to directed acyclic graphs (DAGs) and the algorithms for the updating of probabilistic uncertainty in response to evidence, and statistical learning of model parameters and structures.


Prerequisites: An undergraduate course in probability and statistics, an undergraduate course in discrete mathematics and algorithms,

Syllabus :
The course will consist of two parts: (I) Probabilistic methods and concepts and learning theory associated with Bayesian networks, and (II) algorithmic graph theory applied to belief updating

  • Causality and directed acyclic graphs, and d-separation, conditional independence
  • Markov properties for directed acyclic graphs and faithfulness.
  • Learning about probabilities
  • Structural learning; MDL, predictive inference
  • Exponential familes and graphical models (Conditional Gaussian distributions)
  • Causality and intervention calculus
  • Chordal and decomposable graphs, moral graphs, junction trees, triangulation
  • Local computation on the junction tree, marginalization operations propagation of probability and evidence, consistency
  • Factor graphs, The Sum -Product algorithm (Wiberg's algorithm)

Literature :

  • T. Koski & J.Noble: Bayesian Networks and Causal Probability Calculus. 2009.Bayesian Networks: An Introduction (2009) published by Wiley. It may be ordered from amazon.co.uk here

Credit points : 7.5 p.

Examination : Homework assignments and computer exercises submitted to the examiner as a report .

FIRST LECTURE: Friday, January 18th of 2013 at 15.15 in room: seminarierummet 3721 (room 3721 7th floor ), institutionen för matematik, KTH, Lindstedtsvägen 25.


LECTURES: Fridays at 15.15-17.00 Room: seminarierummet 3721 (room 3721 7th floor), institutionen för matematik, KTH, Lindstedtsvägen 25.


Homework assignments :

Topics for presentations :

  • List of topics (pdf): click



    • Links and resources




    Course schedule and information

    Timo Koski Lecturer and Examiner

    Address:
    Department of Mathematics
    Royal Institute of Technology
    SE-100 44 Stockholm
    Sweden
    Email: tjtkoski@kth.se
    Phone: +46-8-790 71 34
    Office: 3444
    Published by: Timo Koski
    Updated: 30/8-2007