Bayesian Statistics
Course, Master's level, 1MS900
Expand the information below to show details on how to apply and entry requirements.
Spring 2027 Spring 2027, Uppsala, 33%, On-campus, English Only available as part of a programme
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 18 January 2027–6 June 2027
- Language of instruction
- English
- Entry requirements
-
120 credits including 60 credits in mathematics and/or data science including at least 45 credits in mathematics. Participation in Introduction to Data Science or participation in both Inference Theory II and Regression Analysis. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Application deadline
- 15 October 2026
- Application code
- UU-60502
Admitted or on the waiting list?
Spring 2027 Spring 2027, Uppsala, 33%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 18 January 2027–6 June 2027
- Language of instruction
- English
- Entry requirements
-
120 credits including 60 credits in mathematics and/or data science including at least 45 credits in mathematics. Participation in Introduction to Data Science or participation in both Inference Theory II and Regression Analysis. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
About the course
In contrast to the usual frequentist methods, where the parameters are assumed to be fixed, the Bayesian methods assume that the parameters can be described as random variables. This allows one to begin one's analysis by having so-called priori distributions for the parameters, which can then be updated to so-called a posteriori distributions after taking into account the observational data. Finally, these a posteriori distributions can be used to draw conclusions in the statistical analysis.