STOCHASTIC PROCESSES - 2018/9
Module code: MAT2003
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 C Dr (Maths)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 5
JACs code: G330
Module cap (Maximum number of students): N/A
Prerequisites / Co-requisites
Indicative content includes:
concept of stochastic process;
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|
|School-timetabled exam/test||IN-SEMESTER TEST (50 MINS)||20|
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 at the end of Semester 2; 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
Overall student workload
Independent Study Hours: 117
Lecture Hours: 33
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.
Reading list for STOCHASTIC PROCESSES : http://aspire.surrey.ac.uk/modules/mat2003
Programmes this module appears in
|Mathematics with Statistics BSc (Hons)||1||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Mathematics MMath||1||Optional||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|
|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|
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 2018/9 academic year.