# Statistics A

### Course content

• Conditional distributions based on densities, including conditioning in the Gaussian distribution
• Hierarchical/mixed effects models (theoretical and practical aspects)
• Bayesian models and computations, e.g., conjugate priors, maximum a posteriori estimation, credible intervals
• Software for mixed effects models and Bayesian computations

Education

MSc Programme in Statistics

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

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
Oral
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

Oral feedback will be given to students if they make presentations of exercises in class

ECTS
7,5 ECTS
Type of assessment
Practical written examination, 4 hours under invigilation
Part 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
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

Placement
Block 2
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   (5-6a676e6e67426f63766a306d7730666d)
Saved on the 16-03-2022

### Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students