Course content

Statistical analysis involves first setting up a model for data in terms of certain unknown parameters. Bayesian analysis proceeds by placing a prior distribution on these parameters and then deriving and using relevant aspects of the consequent posterior distribution. Bayesian nonparametrics is the extended branch of such modelling and analyses where the parameter of the model is of very high or infinite dimension, as when one models an unknown density, regression, or link function.  This calls for more complex mathematics and computational schemes than for the classical cases where the parameter is of low dimension. There are links to and implications for machine learning. 

Learning outcome

After having completed the course you will have learned some of the more prominent nonparametric prior constructions and ensuing posterior calcuations:

  • the Dirichlet process;
  • the Beta process;
  • Gaussian processes;
  • bigger hierarchical models;
  • applications with real data.

Admission

Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.

Overlapping courses

10 credits overlap with STK9190 – Bayesian nonparametrics

Teaching

3 hours of lectures/exercises per week.

Examination

Depending on the number of students, the exam will be in one of the following four forms:
1. Only written exam
2. Only oral exam
3. A project paper followed by a written exam.
4. A project paper followed by an oral exam/hearing.
For the latter two the project paper and the exam counts equally and the final grade is based on a general impression after the final exam. (The two parts of the exam will not be individually graded.)

The form of examination will be announced by the teaching staff by 15 October/15 March f