STK4021 ¨C Applied Bayesian Analysis

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
• 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 ¨C Probability and Statistical Modelling
• STK1110 ¨C Statistical Methods and Data Analysis
• STK2100 ¨C Machine Learning and Statistical Methods for Prediction and Classification or STK3100 ¨C Introduction to Generalized Linear Models

Overlapping courses

• 10 credits overlap with STK9021 ¨C Applied Bayesian Analysis.
• 7 credits overlap with STK4020 ¨C Bayesian statistics (discontinued).
• 3 credits overlap with STK4050 ¨C Statistical simulations and computation (discontinued).

Teaching

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.

Examination

Final written exam or final oral exam, which counts 100 % towards