STK4021 – Applied Bayesian Analysis
Schedule, syllabus and examination date
Exams after the reopening
As a general rule, exams will be conducted without physical attendance in the autumn of 2021, even after the reopening. See the semester page for information about the form of examination in your course. See also more information about examination at the MN Faculty in 2021.
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.
Admission to the course
Students admitted at UiO must apply for courses in Studentweb. Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.
Nordic citizens and applicants residing in the Nordic countries may apply to take this course as a single course student.
If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures for international applicants.
Recommended previous knowledge
- STK1100 – Probability and Statistical Modelling
- STK1110 – Statistical Methods and Data Analysis
- STK2100 – Machine Learning and Statistical Methods for Prediction and Classification or STK3100 – Introduction to Generalized Linear Models
- 10 credits overlap with STK9021 – Applied Bayesian Analysis.
- 7 credits overlap with STK4020 – Bayesian statistics (discontinued).
- 3 credits overlap with STK4050 – Statistical simulations and computation (discontinued).
3 hours of lectures/exercises per week throughout the semester.
The course may be taught in Norwegian if the lecturer and all students at the first lecture agree to it.
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 written exam or final oral exam, which counts 100 % towards the final grade.
The form of examination will be announced by the lecturer by 15 October/15 March for the autumn semester and the spring semester respectively.
This course has 1 mandatory assignment that must be approved before you can sit the final exam.
It will also be counted as one of the three attempts to sit the exam for this course, if you sit the exam for one of the following courses: STK9021 – Applied Bayesian Analysis
Examination support material
Written examination: Approved calculators are allowed. Information about approved calculators in Norwegian.
Oral examination: No examination support material is allowed.
Language of examination
Courses 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 scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.
Resit an examination
This course offers both postponed and resit of examination. Read more: