NMAK16010U Graphical Models
MSc Programme in Statistics
- Markov kernels and conditional distributions
- Probabilistic conditional independence
- Conditional independence models
- Markov properties on directed and undirected graphs
- Bayesian networks
- Gaussian graphical models
Basic knowledge of the topics covered
- Understand simple properties of conditional distributions and Markov kernels
- Discuss and understand issues concerning conditional distributions and the interplay between probabilistic and other types of conditional independence
- Ability to use standard software packages for the analysis of simple graphical models
- Understand graph based Markov properties and their role for simplification of computation and interpretation
- Understand properties and limitations of methods for estimating graph structure
Examples of course literature
Previous years have used
S. Lauritzen: Lectures on Graphical Models. Department of Mathematical Sciences, University of Copenhagen, 2018
plus parts of S. Højsgaard, D. Edwards, S. Lauritzen. Graphical Models with R.
Springer-Verlag, New York, 2012.
I.e. Measures and Integrals + Mathematical Statistics or equivalent.
Academic qualifications equivalent to a BSc degree is recommended.
Students receive feedback at the exercise sessions.
- 7,5 ECTS
- Type of assessment
- Written assignment, 27 hoursWritten take-home assignment
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
As ordinary exam
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
- Practical exercises
- Theory exercises