|Advanced Bayesian Inference||5 Cr||Föreläsningskurs||15.3.2021 - 4.5.2021|
|Advanced Bayesian Inference||5 Cr||Allmän tent||5.8.2020 - 5.8.2020|
|Advanced Bayesian Inference||5 Cr||Nättentamen||10.6.2020 - 10.6.2020|
|Advanced Bayesian Inference||5 Cr||Föreläsningskurs||9.3.2020 - 3.5.2020|
|Advanced course in Bayesian statistics||5 Cr||Allmän tent||8.8.2018 - 8.8.2018|
|Advanced course in Bayesian statistics||5 Cr||Allmän tent||13.6.2018 - 13.6.2018|
|Advanced course in Bayesian statistics||5 Cr||Allmän tent||23.5.2018 - 23.5.2018|
|Advanced course in Bayesian statistics||5 Cr||Föreläsningskurs||12.3.2018 - 6.5.2018|
Master's Programme in Mathematics and Statistics is responsible for the course.
The course belongs to the Statistics module.
Students in Master's programmes of Mathematics and Statistics, Life Science Informatics and Data Science
The course is available to students from other degree programmes.
Tidigare studier eller kunskaper
Bachelor studies in mathematics and statistics or equivalent knowledge.
It is assumed that you have taken at least one of the following courses:
MAT22005 Bayesian inference
DATA11006 Statistical Data Science
LSI35002 Bayesian data analysis
or a course covering basics in Bayesian inference.
The course covers some foundations of Bayesian statistics and its theoretical links to decision theory. After the course you will understand the justification and axiomatic construction for probability as a measure of subjective uncertainty and how this leads to Bayes theorem. You will also be introduced to properties of Bayesian inference in the limit of large data and to De'Finetti's Theorem and their basic consequences and interpretations. After the course you will understand what are model's marginal likelihood and Bayes factors, posterior predictive model comparison and validation, decision analysis and experimental design. You will also be able to apply these techniques to practical data analysis tasks.
|After the Bachelor studies and Bayesian Inference course||4th period|
After the Bachelor studies and Bayesian Inference course in the 4th period. The course is lectured in every even year (2018, 2020, ...). In odd years, the course is available for self study.
Week 1. Revisal of basics and robust regression Week 2: Model's marginal likelihood and Bayesian inference in the limit of large data Week 3: Probability as a measure of uncertainty and Decision theory Week 4: Score functions and model comparison Week 5: Model comparison and selection with cross validation and information criteria Week 6-7: Design of experiments
Aktiviteter och undervisningsmetoder som stöder lärandet
Lectures, exercises and exercise groups
Lecture notes, Chosen sections from the Bayesian data analysis book, and articles to be announced during the course
Bedömningsmetoder och kriterier
Exam. Course will be graded with grades 1-5. Exercises provide extra points to the exam.
Rekommenderade valfria studier
MAST32001 Computational statistics
MAST32005 Spatial modelling and Bayesian inference