Postgraduate Course: Stochastic Modelling (MATH11029)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Not available to visiting students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Mathematics |
Other subject area | Operational Research |
| Course website |
http://student.maths.ed.ac.uk |
Taught in Gaelic? | No |
| Course description | Syllabus summary: Probability review: Conditional probability, basic definition of stochastic processes. Discrete-time Markov chains: Modelling of real life systems as Markov chains, transient behaviour, limiting behaviour and classification of states, first passage and recurrence times, absorption problems, ergodic theorems, Markov chains with costs and rewards, reversibility. Poisson processes: Exponential distribution, counting processes, alternative definitions of Poisson processes, splitting, superposition and uniform order statistics properties, non-homogeneous Poisson processes. Continuous-time Markov chains: transient behaviour, limiting behaviour and classification of states in continuous time, ergodicity, basic queueing models. |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Course Delivery Information
|
| Delivery period: 2014/15 Semester 2, Not available to visiting students (SS1)
|
Learn enabled: Yes |
Quota: None |
|
Web Timetable |
Web Timetable |
| Course Start Date |
12/01/2015 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Seminar/Tutorial Hours 10,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
66 )
|
| Additional Notes |
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
|
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | MSc Stochastic Modelling | 2:00 | |
Summary of Intended Learning Outcomes
1. Basic understanding of stochastic processes and their characterization
2. Basic probabilistic reasoning skills
3. Ability to model dynamic systems with noise, applications include reliability theory, inventory theory, queueing theory, telecommunication networks, biological systems
4. Ability to classify states of a Markov chain
5. Understanding transient and stationary behaviour of Markov chains and deriving stationary distributions
6. Ability to model and analyze arrival processes as Poisson processes |
Assessment Information
| See 'Breakdown of Assessment Methods' and 'Additional Notes' above. |
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 | SM_OR |
Contacts
| Course organiser | Dr Tibor Antal
Tel: (0131 6)51 7672
Email: Tibor.Antal@ed.ac.uk |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: f.c.reid@ed.ac.uk |
|
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