Statistics A
Course 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
MSc Programme in Statistics
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 lectures and 4 hours of exercises per week for 7 weeks.
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.
Written feedback will be given on mandatory assignments in order for students to improve their subsequent assignments
- ECTS
- 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.
- 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 he/she has mastered the learning outcome of the course.
Single subject courses (day)
- Category
- Hours
- Lectures
- 28
- Preparation
- 121
- Theory exercises
- 28
- Exam Preparation
- 25
- Exam
- 4
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NMAK20003U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Schedulegroup
-
B
- Capacity
- No limit
- Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinator
- Helle Sørensen (5-6e6b72726b4673677a6e34717b346a71)
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Courseinformation of students