Statistics A
Course content
- Conditional distributions based on densities, including conditioning in the Gaussian distribution
- Hierarchical/mixed-effects models (theoretical and practical aspects)
- Bayesian analyses and computations, e.g., prior and posterior distributions, credible intervals, MCMC sampling
- Software for mixed effects models and Bayesian computations
MSc Programme in Statistics
MSc Programme in Mathematics-Economics
Knowledge
- Conditional densities and their relations to joint and marginal densities
- Principles behind Bayesian statistics
- Differences between fixed and random effects in mixed effects 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-effecs models and hierarchical models, using appropriate software
Competencies: Ability to
- identify relevant mixed effects models and hierarchical models (for concrete data examples)
- present and discuss results from analyses statistical based on mixed-effecs models and hierarchical models
- choose between principles for statistical analysis
4 hours of lectures for 7 weeks, 4 hours of exercises per week for 8 weeks (including three multiple choice tests)
Essential prerequisites: Probability distributions with densities, linear normal models, logistic and Poisson regression, R usage (all corresponding to courses “StatMet” and “MStat” (alternatively “MatStat” from previous years) and "Discrete Models" or "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 voluntary assignments.
Oral feedback will be given to students if they make presentations of exercises in class.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Continuous assessment under invigilation
- Type of assessment details
- The assessment is composed of two elements. The first element consists of three individual quizzes, of which the two best count a total of 50% in the final grade. The quizzes will be of one hour each and must be taken during classes (physical attendance, under surveillance). The second element is a 3 hour individual written test which counts 50% in the final grade. It must be taken during class Friday in the exam week (physical attendance, under surveillance). All aids are allowed for quizzes as well as the written test.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
Criteria for exam assessment
The student must in a satisfactory way demonstrate that she/he has mastered the learning outcome of the course.
Single subject courses (day)
- Category
- Hours
- Lectures
- 28
- Preparation
- 118
- Theory exercises
- 29
- Exam Preparation
- 25
- Exam
- 6
- 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 limit
The number of seats may be reduced in the late registration period - Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinator
- Helle Sørensen (5-6a676e6e67426f63766a306d7730666d)
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Courseinformation of students