Course content

Traditional Bayesian methodology works with a finite and often a moderately small number of parameters. The aim of this course is to learn how Bayesian methods may be made to work also in high-dimensional and nonparametric models, where the unknowns are curves, functions, or surfaces (e.g. distribution functions, densities, hazard rates, regression functions, link functions), rather than finitely many parameters. This involves constructing probability measures on big spaces, and to pass from prior processes to posterior processes, which for practical purposes often involve setting up simulation schemes of the MCMC variety. Topics include Dirichlet and Beta processes, Gaussian processes, and mixtures.

Learning outcome

Students become familiar with the more important nonparametric Bayesian constructions, with e.g. Dirichlet process priors and Gaussian processes. They will also learn how to set such ideas into statistical practice.

Admission

PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.

If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.

PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.

Prerequisites

Recommended previous knowledge

Master's degree in statistics, or working knowledge on a similar level.

Overlapping courses

For information about the potential partial overlap with other courses, contact the Department.

Teaching

Each week, two hours of lectures plus one hour of exercises. If a small number of students are following the course, it may be organised on a self-reading basis, with one weekly hour of joint or individual supervision.

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.)

What form the exam will take will be announced by the teaching staff within October 15th for the autumn semester and March 15th for the spring semester.

Examination support material

Permitted aids at the exam if written: Approved calculator.
Oral exam: No aids permitted.

Information about approved calculators (Norwegian only)

Language of examination

Subjects taught in English will only offer the exam paper in English.

You may write your examination paper in Norwegian, Swedish, Danish or English.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Explanations and appeals

Resit an examination

This course offers both postponed and resit of examination. Read more:

Withdrawal from an examination

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.

Evaluation

The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.

Facts about this course

Credits

10

Level

PhD

Teaching

Taught according to demand and resources. If you want to attend the course, please send an e-mail to studieinfo@math.uio.no.

Examination

According to demand and resources.

Teaching language

English

The course is given in English. If no students have asked for the course in English within the first lecture, it may be given in Norwegian.