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: 2010/11 Semester 2, Available to all students (SV1)
|
WebCT enabled: Yes |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| King's Buildings | Lecture | | 1-11 | | 10:00 - 10:50 | | | | | King's Buildings | Lecture | | 1-11 | | | | | 10:00 - 10:50 |
| First Class |
Week 1, Tuesday, 10:00 - 10:50, Zone: King's Buildings. Ashworth, Th 1 |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
Stationery Requirements |
Comments |
| Main Exam Diet S2 (April/May) | | 2:00 | 20 sides. No YAF | c/w MATH09018, MATH11029 | | Resit Exam Diet (August) | | 2:00 | 20 sides. No YAF | c/w MATH09018, MATH11029 |
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
| Examination only. |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords |
SMo |
Contacts
| Course organiser |
Dr Adri Olde-Daalhuis
Tel: (0131 6)50 5992
Email: A.OldeDaalhuis@ed.ac.uk |
Course secretary |
Mrs Kathryn Mcphail
Tel: (0131 6)50 4885
Email: k.mcphail@ed.ac.uk |
|
copyright 2011 The University of Edinburgh -
31 January 2011 7:59 am
|
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