Statistics A
Course content
- Conditional distributions (mainly with densities, including conditioning in the Gaussian distribution
- Hierarchical/mixed models (theoretical and practical aspects)
- Bayesian analyses and computations (prior and posterior distributions, credible intervals, MCMC sampling, etc.)
- Software for mixed models and Bayesian computations
MSc Programme in Statistics
MSc Programme in Mathematics-Economics
MSc Programme in Actuarial Mathematics
Knowledge
- Conditional densities and their relations to joint and marginal densities
- Principles behind Bayesian statistics
- Differences between fixed and random effects in mixed models
- Methods for computations in posterior distributions
Skills: Ability to
- do computations with conditional and marginal densities, in particular with prior and posterior densities and with the Gaussian distribution
- carry out Bayesian estimation and inference with explicit formulas (when available) and with appropriate sampling techniques
- carry out analyses (Bayesian and frequentistic) with mixed/hierarchical models, using appropriate software
Competencies: Ability to
- identify relevant mixed/hierarchical models (for concrete data examples)
- present and discuss results from analyses statistical based on mixed/hierarchical models
- choose between principles for statistical analysis
4 hours of lectures per week for 7 weeks, 4 hours of exercises per week for 7 weeks, 3 hour test in week 9
Essential prerequisites: Probability distributions with densities, linear normal models, logistic and Poisson regression, R usage (all corresponding to courses “StatMet” and “MStat” and "Regression"). The course requires maturity at the level of MSc students in statistics; it is not an introductory statistics course.
Written feedback will be given on assignments.
Oral feedback will be given to students if they make presentations of exercises in class.
- ECTS
- 7,5 ECTS
- Type of assessment
-
On-site written exam, 4 hours under invigilation
- Type of assessment details
- The students must bring their own computers and prepare the answer as a pdf file.
- Examination prerequisites
-
There will be two group assignments (up to three students). The students have to hand-in these assignments, which then need to get approved.
- Aid
- All aids allowed except Generative AI and internet access
Internet is allowed for download of data and upload of answer.
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Re-exam
-
Same as ordinary exam.
The exam prerequisite must be met by submitting the two assignments and getting them approved no later than three weeks before the re-exam. The course responsible will inform the student/s, about when they will be notified, whether they can take the exam or not.
Criteria for exam assessment
The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Intended learning outcome.
Single subject courses (day)
- Category
- Hours
- Lectures
- 28
- Preparation
- 107
- Theory exercises
- 28
- Project work
- 20
- Exam Preparation
- 20
- Exam
- 3
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NMAK20003U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 2
- Schedulegroup
-
B
- Capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
- Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinator
- Helle Sørensen (5-706d74746d4875697c7036737d366c73)
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