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)
FSF3970 Bayesian Network 7.5 credits
Information per course offering
Course offerings are missing for current or upcoming semesters.
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus FSF3970 (Autumn 2009–)Headings with content from the Course syllabus FSF3970 (Autumn 2009–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
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
Literature
T. Koski & J. Noble: Bayesian Networks: An Introduction (2009) J. Wiley & Sons.
Examination and completion
Grading scale
G
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.
If the course is discontinued, students may request to be examined during the following two academic years.
Computer project and homework assignments.
Other requirements for final grade
The examination is computer project P/F and homework assignments (80 % correct).
Examiner
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
Education cycle
Third cycle