Bayesian Statistics and Data Analysis
Course, Master's level, 2ST128
Autumn 2023 Autumn 2023, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 28 August 2023–1 November 2023
- Language of instruction
- English
- Entry requirements
-
120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science.
- Selection
-
Higher education credits (maximum 285 credits)
- Fees
-
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 15,000
- Total tuition fee: SEK 15,000
- Application deadline
- 17 April 2023
- Application code
- UU-26617
Admitted or on the waiting list?
- Registration period
- 27 July 2023–27 August 2023
- Information on registration from the department
Autumn 2024 Autumn 2024, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 2 September 2024–5 November 2024
- Language of instruction
- English
- Entry requirements
-
120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science.
- Selection
-
Higher education credits (maximum 285 credits)
- Fees
-
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 15,000
- Total tuition fee: SEK 15,000
- Application deadline
- 15 April 2024
- Application code
- UU-26617
Admitted or on the waiting list?
- Registration period
- 25 July 2024–1 September 2024
- Information on registration from the department
About the course
The course is an introduction to Bayesian Statistics and Dana Analysis. The course covers core ideas in Bayesian inference, such as Bayesian epistemology, Bayes theorem, prior and posterior distributions, analytical derivation of posterior distributions and conjugacy. In addition, modern methods for posterior simulation are introduced in the form of Markov Chain Monte Carlo, Hamiltonian Monte Carlo and the probabilistic programming language Stan. The Bayesian inference methods and principles on model assessment and model selection are studied theoretically and practically through continuous computer assignments and a smaller data analysis project.
Reading list
No reading list found.