Admission requirements
The mandatory requirement is completion of Bayesian Methods 1 or of an equivalent course.
Description
Classical statistics offers a powerful toolbox for data analysis. This toolbox, however, may not always be sufficiently flexible for modern data situations. For example, some situations benefit from data integration or the inclusion of information from other sources than your data. The Bayesian framework allows for the integration and inclusion of information from many sources as well as a natural quantification of uncertainty in subsequent analysis. It offers these benefits for standard statistical models as well as highly customized models. This flexibility is the reason why the machinery of Bayesian inference has been successfully used in, for example, code-cracking, self-driving cars, genomic prediction, and climate-change prediction. Bayesian inference now underlies many advances in artificial intelligence, machine learning, and data science. This course offers a hands-on, example-driven approach to the core tools of Bayesian data analysis.
Course Objectives
After successful completion of the course, the students are able to:
Recognize questions and situations that ask for a Bayesian approach to data analysis
Apply modern computational approaches to Bayesian data analysis
Effectively set up a Bayesian data analysis lifecycle
Effectively communicate a Bayesian data analysis lifecycle
Timetable
You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have sucessfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.
MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).
For more information, watch the video or go the the 'help-page' in MyTimetable. Please note: Joint Degree students Leiden/Delft have to merge their two different timetables into one. This video explains how to do this.
Mode of Instruction
Lectures, tutorials, and homework.
Assessment method
Written exam (50%) and homework (50%).
Reading list
Alicia A. Johnson, Miles Q. Ott, Mine Dogucu. Bayes Rules! An Introduction to Applied Bayesian Modeling. ISBN 9780367255398. Chapman & Hall. 2022.
The authors made the book available online for free at: https://www.bayesrulesbook.com/
Material from other sources will be provided as the course progresses.
Registration
It is the responsibility of every student to register for courses with the new enrollment tool MyStudyMap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.
Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.
Extensive FAQ's on MyStudymap can be found here.
Contact
Shota Gugushvili (shota.gugushvili@wur.nl); see also https://gugushvili.github.io/contact/