STOCHASTIC PROCESSES - 2022/3
Module code: MAT2003
In light of the Covid-19 pandemic the University has revised its courses to incorporate the ‘Hybrid Learning Experience’ in a departure from previous academic years and previously published information. The University has changed the delivery (and in some cases the content) of its programmes. Further information on the general principles of hybrid learning can be found at: Hybrid learning experience | University of Surrey.
We have updated key module information regarding the pattern of assessment and overall student workload to inform student module choices. We are currently working on bringing remaining published information up to date to reflect current practice in time for the start of the academic year 2021/22.
This means that some information within the programme and module catalogue will be subject to change. Current students are invited to contact their Programme Leader or Academic Hive with any questions relating to the information available.
Realistic modelling often requires the inclusion of stochastic (as opposed to deterministic) elements. In this module we study a large class of stochastic processes, that is, probabilistic models for series of events.
WULFF Claudia (Maths)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
JACs code: G330
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 106
Seminar Hours: 11
Guided Learning: 11
Captured Content: 22
Prerequisites / Co-requisites
Indicative content includes:
- concept of stochastic process;
- random walks;
- properties of Markov chains: recurrence and transience, periodicity, communicating classes, irreducibility;
- first step analysis;
- Basic Limit Theorem, stationary distributions, and their applications;
- Markov processes in continuous time: derivation of the Poisson process and generalised birth and death process.
|Assessment type||Unit of assessment||Weighting|
The assessment strategy is designed to provide students with the opportunity to demonstrate
· Understanding of and ability to interpret and manipulate mathematical statements.
· Subject knowledge through the recall of key definitions, theorems and their proofs.
· Analytical ability through the solution of unseen problems in the in-semester test and in the exam.
Thus, the summative assessment for this module consists of:
· One two hour examination; worth 80% of the module mark.
· One in-semester test; worth 20% of the module mark.
Formative assessment and feedback
Students receive written feedback via marked unassessed coursework. In addition, verbal feedback is provided by lecturer/class tutor at biweekly tutorial lectures.
- This module aims to introduce students to stochastic processes and their applications.
|1||Understand the properties of stochastic processes||KCP|
|2||apply this knowledge to analyse specific stochastic processes, occurring for example in finance or biology.||KCPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to introduce students to the theory of stochastic processes.
The learning and teaching methods include:
- 3 hour lectures per week x 11 weeks, on the blackboard and Q + A opportunities for students;
- Including (every second week) a tutorial lecture for guided discussion of solutions to problem sheets or unassessed coursework provided to and worked on by students in advance.
- Including revision lectures (in week 12).
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Upon accessing the reading list, please search for the module using the module code: MAT2003
Programmes this module appears in
|Mathematics MMath||1||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics with Statistics MMath||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics with Statistics BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics BSc (Hons)||1||Optional||A weighted aggregate mark of 40% is required to pass the module|
|Financial Mathematics BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics with Music BSc (Hons)||1||Optional||A weighted aggregate mark of 40% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.