STK9021 – Applied Bayesian Analysis
Course description
Schedule, syllabus and examination date
Course content
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.
Learning outcome
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
- know about and can use 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.
Admission to the course
PhD candidates from the Faculty of Mathematics and Natural Sciences at 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
- 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
Overlapping courses
- 10 credits overlap with STK4021 – Applied Bayesian Analysis.
- 7 credits overlap with STK4020 – Bayesian statistics (discontinued).
- 3 credits overlap with STK4050 – Statistical simulations and computation (discontinued).
Teaching
4 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.
Examination
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 1 October/1 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.
In addition, each PhD candidate 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 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: STK4021 – 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.
Grading scale
Grades are awarded on a pass/fail scale. Read more about the grading system.
Resit an examination
This course offers both postponed and resit of examination. Read more:
More about examinations at UiO
- Use of sources and citations
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.