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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2023/2024

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: Bayesian Data Analysis (MATH11175)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThe 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Bayesian Theory (MATH11177)
Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) 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 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
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)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Solve practical statistical modelling problems using JAGS.
  2. Choose and apply appropriate Bayesian statistical models and interpret the results.
  3. Prepare written reports based on Bayesian statistical analysis.
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.
Additional Information
Graduate Attributes and Skills Not entered
KeywordsBDAn,bayesian,data analysis,statistics
Contacts
Course organiserDr Daniel Paulin
Tel:
Email: dpaulin@ed.ac.uk
Course secretaryMr Jack Draper
Tel:
Email: v1jdrape@ed.ac.uk
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