Undergraduate Course: Stochastic Modelling (MATH10007)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Credits | 10 |
| Home subject area | Mathematics |
Other subject area | Specialist Mathematics & Statistics (Honours) |
| Course website |
https://info.maths.ed.ac.uk/teaching.html |
Taught in Gaelic? | No |
| Course description | Core course for Honours Degrees involving Statistics; optional course for Honours degrees involving Mathematics.
Syllabus summary: Markov Chains in discrete time: classification of states, first passage and recurrence times, absorption problems, stationary and limiting distributions. Markov Processes in continuous time: Poisson processes, birth-death processes. The Q matrix, forward and backward differential equations, imbedded Markov Chain, stationary distribution. |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
|
| Delivery period: 2013/14 Semester 2, Available to all students (SV1)
|
Learn enabled: Yes |
Quota: None |
Web Timetable |
Web Timetable |
| Course Start Date |
14/01/2014 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
71 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
95 %,
Coursework
5 %,
Practical Exam
0 %
|
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
|
|
| Main Exam Diet S2 (April/May) | | 2:00 | | | | Resit Exam Diet (August) | | 2:00 | | |
Summary of Intended Learning Outcomes
1. Ability to solve difference equations using generating functions, using P.S.+C.S.
2. Ability to classify states of a Markov Chain.
3. Ability to calculate mean first passage and recurrence times for an irreducible recurrent state Markov Chain.
4. Calculation of absorption probabilities for a Markov Chain with recurrent classes and transient states.
5. Understanding stationary and limiting behaviour and deriving these probability distributions.
6. Appreciating the range of applications, together with a facility to model appropriate problems in terms of a stochastic process.
|
Assessment Information
| See 'Breakdown of Assessment Methods' and 'Additional Notes' above. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Markov Chains in discrete time: classification of states, first passage and recurrence times, absorption problems, stationary and limiting distributions.
Markov Processes in continuous time: Poisson processes, birth-death processes.
The Q matrix, forward and backward differential equations, imbedded Markov Chain, stationary distribution. |
| Transferable skills |
Not entered |
| Reading list |
http://www.readinglists.co.uk |
| Study Abroad |
Not Applicable. |
| Study Pattern |
See 'Breakdown of Learning and Teaching activities' above. |
| Keywords | SMo |
Contacts
| Course organiser | Dr Burak Buke
Tel: (0131 6)50 5086
Email: b.buke@ed.ac.uk |
Course secretary | Mrs Kathryn Mcphail
Tel: (0131 6)50 4885
Email: k.mcphail@ed.ac.uk |
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© Copyright 2013 The University of Edinburgh - 10 October 2013 4:51 am
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