Decision Theory, 6 credits

Main field of study

Course level

Advancement level

Course offered for

• Master's Programme in Statistics and Machine Learning

Entry requirements

• Bachelor's degree equivalent to a Swedish Kandidatexamen of 180 ECTS credits in one of the following subjects:
  • statistics
  • mathematics
  • applied mathematics
  • computer science
  • engineering
• Completed courses in
  • calculus
  • linear algebra
  • statistics
  • machine learning
  • programming
• English corresponding to the level of English in Swedish upper secondary education (Engelska 6/B)
  (Exemption from Swedish

Intended learning outcomes

After completion the course, the student should on an advanced level be able to:
- use statistical methods for decision making,
- apply the principles for subjective probability interpretation, Bayesian inference, utility theory and sequential analysis in order to make a decision,
- critical assess the presumptions for each step in a decision making process

Course content

The course content comprises:
- The subjective interpretation of probabilities 
- Probabilistic reasoning and likelihood theory,
- Bayesian hypothesis evaluation,
- Decision theoretic elements
- Utility and loss functions
- Graphical modelling as a tool for decision making
- Sequential analysis

Teaching and working methods

Assignments encompassing both theoretical and computer-based exercises. One final oral examination.
Detailed information about the examination can be found in the course’s study guide. 

Examination

Assignments encompassing both theoretical and computer-based exercises. One final oral examination.
Detailed information about the examination can be found in the course’s study guide. 

Grades

Department