NMAK20003U Statistics A

Volume 2020/2021

MSc Programme in Statistics

  • 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


  • 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
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.
4 hours lectures and 4 hours of exercises per week for 7 weeks.
  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 121
  • Theory exercises
  • 28
  • Exam Preparation
  • 25
  • Exam
  • 4
  • Total
  • 206
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

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.
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.

All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship

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.