NMAK20003U Statistics A
Volume 2020/2021
Education
MSc Programme in Statistics
Content
- Conditional distributions based on densities
- Conditioning in the Gaussian distribution
- Hierarchical/mixed effects models
- Bayesian models and computations, e.g., conjugate priors, maximum a posteriori estimation, credible intervals
- Software for mixed effects models and Bayesian computations
Learning Outcome
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
Recommended Academic Qualifications
Essential prerequisites:
Probability distributions with densities, linear normal models,
logistic and Poisson regression, R usage (all corresponding to
courses "Mathematical Statistics", and "Discrete
Models" or "Regression"). The course requires
maturity at the level of MSc students in statistics; it is not an
introductory statistics course.
Teaching and learning methods
4 hours lectures and 4 hours
of exercises per week for 7 weeks.
Workload
- Category
- Hours
- Lectures
- 28
- Preparation
- 121
- Theory exercises
- 28
- Exam Preparation
- 25
- Exam
- 4
- Total
- 206
Feedback form
Written
Continuous feedback during the course of the semester
Written feedback will be given on mandatory assignments in order for students to improve their subsequent assignments
Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Practical written examination, 4 hours under invigilationPart of the exam consists of data analysis which must be carried out with software used in the course. A USB-stick with data is handed out to the students along with the assignment sheet.
- Exam registration requirements
There will be three group assignments. The students must hand-in these assignments, in groups up to three students, and have them approved.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
- Re-exam
Same as the ordinary exam. If the mandatory assignments have not been approved during the course the non-approved assignment(s) must be handed in no later than three weeks before the beginning of the re-exam week. The assignments must be approved before the re-exam.
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
- NMAK20003U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- B
- Course capacity
- No limit
- Course is also available as continuing and professional education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinators
- Helle Sørensen (5-6c69707069447165786c326f7932686f)
Saved on the
14-05-2020