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FSF3970 Bayesian Network 7.5 credits

Administer About course

Course offerings are missing for current or upcoming semesters.
Headings with content from the Course syllabus FSF3970 (Autumn 2009–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • 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)

Intended learning outcomes

By the end of the course, the participants 

  • Will be able to assess when to use a Bayesian network as a model for an interaction of several variables.

  • will be able to  identify  statements of conditional independence by a DAG.

  • will be able to use at least two algorithms to learn the structure of a Bayesian network from data 

  • will be able to use available software for update of probabilities

  • will be able to assess the nature of statements of  causality in a  statistical model.      

Literature and preparations

Specific prerequisites

Masters degree in mathematics, or in computational mathematics or in computer science/engineering with  at least 30 cu in mathematics and  20 cu in statistics.  

Suitable course: SF2740 Graph theory 7,5 hp 

Recommended prerequisites

No information inserted

Equipment

No information inserted

Literature

T. Koski & J. Noble:  Bayesian Networks: An Introduction (2009)   J. Wiley & Sons. 

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F

Examination

    Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

    The examiner may apply another examination format when re-examining individual students.

    Computer project and  homework assignments.

    Other requirements for final grade

    The examination is computer project  P/F and  homework assignments (80 % correct).

    Opportunity to complete the requirements via supplementary examination

    No information inserted

    Opportunity to raise an approved grade via renewed examination

    No information inserted

    Examiner

    Profile picture Timo Koski

    Ethical approach

    • All members of a group are responsible for the group's work.
    • In any assessment, every student shall honestly disclose any help received and sources used.
    • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

    Further information

    Course room in Canvas

    Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

    Offered by

    SCI/Mathematics

    Main field of study

    This course does not belong to any Main field of study.

    Education cycle

    Third cycle

    Add-on studies

    No information inserted

    Contact

    Timo Koski (tjtkoski@kth.se); 08-790 71 34

    Postgraduate course

    Postgraduate courses at SCI/Mathematics