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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2019/2020

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: MIGS: Modern Regression and Bayesian Methods (MATH11218)

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 Credits15 ECTS Credits7.5
SummaryThis course will cover the following-

- Random effects models
- Modern regression (generalised additive models, etc)
- Markov chain Monte Carlo methods
- Case studies
Course description The aim is to learn new things to get a broad education in the area as a basis for a wide range of PhD projects and for post-PhD employment. Unless otherwise noted, the details of the content of these courses can be found on the Scottish Mathematical Sciences Training Centre web site www.smstc.ac.uk
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2019/20, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 150 ( Lecture Hours 20, Programme Level Learning and Teaching Hours 3, Directed Learning and Independent Learning Hours 127 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% coursework
Feedback Not entered
No Exam Information
Learning Outcomes
Further develop students' understanding of some key topics at the heart of research in statistical methods.
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Contacts
Course organiserProf Benedict Leimkuhler
Tel:
Email: B.Leimkuhler@ed.ac.uk
Course secretaryMrs Katy Cameron
Tel: (0131 6)50 4899
Email: Katy.Cameron@ed.ac.uk
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