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

Learning outcome


  • 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-effects/hierarchical models, using appropriate software

Competencies: Ability to

  • identify relevant mixed-effects/hierarchical models (for concrete data examples)
  • present and discuss results from analyses statistical based on mixed-effects/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” (alternatively “MatStat” from previous years) and "Regression"). The course requires maturity at the level of MSc students in statistics; it is not an introductory statistics course.

Continuous feedback during the course of the semester

Written feedback will be given on assignments.

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

7,5 ECTS
Type of assessment
Continuous assessment under invigilation
Type of assessment details
The assessment is composed of two written group assignments during the course and a 3 hour individual written test by the end of the course. The group assignments count 15% each in the final assessment. Groups are expected to consist of 2-3 students, and group members must explain their contribution to the solutions handed in by the group. The individual test counts 70% in the final assessment. The test takes place during class Friday in the exam week (physical attendance, under invigilation). In order to pass, the student must hand in at least one of the group assignments, participate in the individual test, and obtain a satisfactory combined result.
All aids allowed

It is allowed to use Large Language Models (LLM)/Large Multimodal Models (LMM), e.g. ChatGPT and GPT-4, for the group assignments. The students must write whether they used these tools and for what purposes.

Marking scale
7-point grading scale
Censorship form
External censorship
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


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 2
No limit
The number of seats may be reduced in the late registration period
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Helle Sørensen   (5-716e75756e49766a7d7137747e376d74)
Saved on the 24-08-2023

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