NMAK15014U Gaussian Graphical Models
Volume 2015/2016
Education
MSc programme in Statistics
Content
- The multivariate Gaussian distribution; definition and basic properties
- The Wishart distribution; definition and basic properties
- The Markov property on undirected graphs
- Conditional independence in the multivariate Gaussian distribution
- Gaussian graphical models: definition and basic likelihood theory
- Decomposable Gaussian graphical models
- Model determination in Gaussian graphical models
- The graphical lasso
- Wishart and inverse Wishart distributions on Gaussian graphical models
- Bayesian model determination in Gaussian graphical models
Learning Outcome
Knowledge:
Basic knowledge of the topics covered
Skill:
- discuss and understand issues of conditional independence in Gaussian graphical models
- discuss and understand basic algorithms for estimation and model determination in Gaussian graphical models
- Ability to analyse simple multivariate datasets using Gaussian graphical models using standard software packages
Competences:
- discuss and understand the role of decomposability for Gaussian graphical models
- discuss and understand the role, similarities, and differences between model determination methods in Gaussian graphical models
Literature
Excerpts from suitable monographs and lecture notes.
Academic qualifications
Some basic understanding
of statistical concepts and statistical distribution theory,
including conditional distributions and conditioning eg Stat 1+2
plus Beting
Teaching and learning methods
6 weeks with 4 hours of
lectures and 2 hours of exercises
1 week with 4 hours of lectures and 4 hours of practical exercises
1 week with 4 hours of lectures and 4 hours of practical exercises
Workload
- Category
- Hours
- Exam
- 27
- Lectures
- 28
- Practical exercises
- 4
- Preparation
- 135
- Theory exercises
- 12
- Total
- 206
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Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Written assignment, 27 hoursWritten homework.
- Exam registration requirements
To register for the exam, the student has to present exercises in class
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner
- Re-exam
As ordinary exam. If the student has not presented the required exercises in class in order to register for the exam, he/ she can fulfil the requirements by presenting an exercise to the professor at the latest two weeks prior to the beginning of the re-exam week.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- NMAK15014U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 1
- Schedule
- C
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Steffen L. Lauritzen (9-766b7f7c737e846f784a776b7e7238757f386e75)
Phone +45 35 33 75 97; room
04.4.16
Lecturers
Steffen L. Lauritzen
Saved on the
27-04-2015