Postgraduate Course: Bayesian Data Analysis (MATH11175)
Course Outline
School | School of Mathematics |
College | College of Science and Engineering |
Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
SCQF Credits | 10 |
ECTS Credits | 5 |
Summary | The course will provide the students with practical experience of applying Bayesian analyses to a range of statistical models. The statistical analyses will be conducted using the widely used computer package JAGS. An introduction to JAGS will be provided with additional hands-on experience. Assessment will be by written reports of Bayesian data analyses. |
Course description |
1. Basic principles of applied Bayesian analyses and reproducibility.
2. Introduction to probabilistic programming packages.
3. Generalised linear models with applications to real data using probabilistic programming packages.
4. Mixed effects models.
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Entry Requirements (not applicable to Visiting Students)
Pre-requisites |
Students MUST have passed:
Bayesian Theory (MATH11177)
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Co-requisites | |
Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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Academic year 2024/25, Not available to visiting students (SS1)
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Quota: None |
Course Start |
Semester 2 |
Timetable |
Timetable |
Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 16,
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
72 )
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Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
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Additional Information (Assessment) |
Coursework 50%, Examination 50% |
Feedback |
Written feedback will be provided on the coursework assignments. Students will also receive oral feedback about their progress during the workshops and office hours. |
Exam Information |
Exam Diet |
Paper Name |
Hours & Minutes |
|
Main Exam Diet S2 (April/May) | | 1:30 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Solve practical statistical modelling problems using JAGS.
- Choose and apply appropriate Bayesian statistical models and interpret the results.
- Prepare written reports based on Bayesian statistical analysis.
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Reading List
Bayesian Data Analysis (3rd edition). Gelman, Carlin, Stern, Dunson, Vehtari and Rubin. CRC Press
Core statistics. Wood, Simon N. Cambridge University Press, 2015.
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Additional Information
Graduate Attributes and Skills |
Not entered |
Keywords | BDAn,bayesian,data analysis,statistics |
Contacts
Course organiser | Dr Jordan Richards
Tel:
Email: jricha3@ed.ac.uk |
Course secretary | Miss Kirstie Paterson
Tel:
Email: Kirstie.Paterson@ed.ac.uk |
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