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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
MVE170 - Basic stochastic processes  
 
Syllabus adopted 2014-02-13 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

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0107 Examination 7,5c Grading: TH   7,5c   09 Jan 2017 pm H,  12 Apr 2017 pm SB   24 Aug 2017 pm SB  

In programs

TKTFY ENGINEERING PHYSICS, Year 3 (elective)
TKTEM ENGINEERING MATHEMATICS, Year 2 (compulsory)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (elective)
TKAUT AUTOMATION AND MECHATRONICS ENGINEERING, Year 3 (elective)

Examiner:

Docent  Patrik Albin



  Go to Course Homepage

Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

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:

- 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.

Geoffrey Grimmett och David Stirzaker: Probability and Random Processes, 3rd Edition. Oxford 2001.

Examination

Written exam.


Page manager Published: Thu 04 Feb 2021.