STK9021 – Applied Bayesian Analysis and Numerical Methods
Schedule, syllabus and examination date
Combining various data sources and other types of information is becoming increasingly important in various types of analyses. Certain classes of Bayesian hierarchical models have shown to be particularly useful in such contexts. Bayesian approaches are strongly connected to statistical computational methods, and in particular to Monte Carlo techniques. This course considers the foundation of Bayesian analysis, how to use Bayesian methods in practice, and computational methods for hierarchical models.
After completing the course you:
- can handle the general Bayesian principles and the foundation for Bayesian analysis;
- have knowledge about how a priori insight can be formulated as a priori distributions through Bayes’ formula;
- know of the relations between Bayesian and non-Bayesian methods, including empirical Bayes methods;
- have knowledge about the principles behind hierarchical models;
- can handle various computational methods for simple and hierarchical models (including asymptotic considerations, Monte Carlo methods and Markov Chain Monte Carlo methods);
- are able to use the computational methods taught in the course on real problems and data, and also interpret the results;
- will be able to present, on a scientific level, a short thesis on a chosen topic of relevance, selected in collaboration with the lecturer.
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.
Recommended previous knowledge
- 10 credits overlap with STK4021 – Applied Bayesian Analysis and Numerical Methods
- 7 credits overlap with STK4020 – Bayesian statistics (discontinued)
- 7 credits overlap with STK9020 – Bayesian statistics (discontinued)
- 3 credits overlap with STK4050 – Statistical simulations and computation (discontinued)
- 3 credits overlap with STK9050 – Statistical simulations and computation (discontinued)
3 hours of lectures/exercises per week.
Upon the attendance of three or fewer students, the lecturer may, in conjunction with the Head of Teaching, change the course to self-study with supervision.
Final oral or written examination. The form of examination will be announced by the teaching staff by 15 October/15 March for the autumn semester and the spring semester respectively.
In addition, each PhD student is expected to give an oral presentation on a topic of relevance chosen in cooperation with the lecturer. The presentation has to be approved by the lecturer for the student to be admitted to the final exam.
Examination support material
Written examination: Approved calculators are allowed.
Information about approved calculators (Norwegian only)
Oral examination: No resources are allowed.
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.
Grades are awarded on a pass/fail scale. Read more about the grading system.
Explanations and appeals
Resit an examination
Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester.
Re-scheduled examinations are not offered to students who withdraw during, or did not pass the original examination.
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.
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.