Modern Topics in Statistics

Course content

The course will be concerned with selected modern topics in statistics such as, for example, network analysis, Bayesian computation, non-parametric Bayesian inference.

Education

MSc Programme in Statistics

Learning outcome

Knowledge:

  • Basic knowledge of the topics covered
     

Skills:

  • Understand basic elements of models dicussed in the course
  • Discuss and understand issues involved in the analysis of models 
  • Discuss and understand properties and limitations of the methods associated with the models covered in the course
     

Competences:

  • Ability to perform basic Bayesian computations and a basic network analysis 
  • Understanding the role of the models in analysing data

Four hours of lectures and three hours of student presentations per week for 8 weeks.

Lecture notes, selected chapters from books, and selected articles from the literature

Basic mathematical statistics and probability based on measure theory.
For example: Measures and Integrals + Stat1 + Stat2 or equivalent.

ECTS
7,5 ECTS
Type of assessment
Continuous assessment
Students will have to give three presentations during the course, each of which will count equally.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Criteria for exam assessment

Students must demonstrate understanding of topics covered

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 32
  • Theory exercises
  • 24
  • Course Preparation
  • 120
  • Exam
  • 30
  • English
  • 206