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Syllabus for

Academic year
MVE170 - Basic stochastic processes
 
Syllabus adopted 2013-02-21 by Head of Programme (or corresponding)
Owner: MPENM
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Mathematics
Department: 11 - MATHEMATICAL SCIENCES


Teaching language: English
Open for exchange students
Block schedule: LA

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0107 Examination 7,5c Grading: TH   7,5c   16 Dec 2013 pm V,  22 Apr 2014 pm V,  25 Aug 2014 am V

In programs

MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (elective)
TKIEK INDUSTRIAL ENGINEERING AND MANAGEMENT - Financial mathematics, Year 3 (elective)
TKTFY ENGINEERING PHYSICS, Year 3 (elective)
TKTEM ENGINEERING MATHEMATICS, Year 2 (compulsory)

Examiner:

Docent  Patrik Albin


Course evaluation:

http://document.chalmers.se/doc/82b98243-412b-4f55-beac-2510fa963afe


  Go to Course Homepage

Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

Course specific prerequisites

Basic probability theory on the level of an undergraduate course in mathematical statistics.

Aim

The course is a self-contained introduction to basic stochastic models
providing a wide range of examples of applications that illustrate the
models and make the methods of solution clear.

Learning outcomes (after completion of the course the student should be able to)

Upon completion of the course students should have a good
understanding of the theory of the random processes considered
in the course as well as practical experience of them from problem
solving and computer exercises.

Content

Main topics of the course are:

- Review of relevant probability theory, including random variables
in one and several dimensions, covariance, conditional probability
and expectations, chracteristic functions, etc.

- Introduction to random processes in discrete and continuous time,
characterization and classification of random processes.

- Markov chains and Markov processes, Poisson processes and Wiener
processes, martingales.

- Continuity, differetiation and integration of random processes, spectral
analysis, white noise, random processes in linear systems.

- Implementation and analysis of random processes in computers.



Organisation

The course comprises lectures and work with problem solving including computer problems.

Literature

Hwei Hsu: Probability, Random Variables, and Random
Processes, 2nd Edition. Schaum's Outlines, McGraw-Hill 2010.

Examination

Written exam.


Published: Mon 28 Nov 2016.