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Graduate courses

Departments' graduate courses for PhD-students.


Syllabus for

Academic year
MVE051 - Mathematical statistics and discrete mathematics  
Matematisk statistik och diskret matematik
Syllabus adopted 2019-02-21 by Head of Programme (or corresponding)
Owner: TKITE
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: First-cycle
Major subject: Mathematics

Teaching language: Swedish
Application code: 52118
Open for exchange students: No
Only students with the course round in the programme plan

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0113 Written and oral assignments 1,5 c Grading: UG   1,5 c    
0213 Examination 6,0 c Grading: TH   6,0 c   05 Jun 2021 am J,  04 Jan 2021 pm J,  25 Aug 2021 pm J

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Timo Vilkas

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General entry requirements for bachelor's level (first cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

The same as for the programme that owns the course.
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Basic knowledge of discrete mathematics, linear algebra and calculus.


The aim of the course is to give
- understanding of basic knowledge in probability theory, statistics, and combinatorics which is important for technical studies and specifically for studies in information technology
- skills for understanding and using mathematical language
- ability to communicate mathematics

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

- identify problems arising in technical studies and specifically in information technology for which the treatment requires use of fundamental concepts and methods from Probablity theory and Mathematical statistics.
- describe and analyze such problems in terms of statistics and discrete mathematics.
- apply basic statistical mehods such as parameter and interval estimation, testing of statistical hypotheses, and linear regression, in problem solving.


The course covers topics in a number of areas. Within each area relevant mathematical concepts are studied. These concepts are considered on different levels of depth. The topics discussed are:
- Probability theory and Markov chains: random variables, expectation, variance, correlation, conditional probability, the law of large numbers, the central limit theorem.
- Statistics: point estimation, confidence intervals, hypotheses testing.
- Combinatorics: combinations, permutations, generating functions.

In Probability theory, the emphasis is on discrete models.


The teaching is built up around certain themes. The mathematical concepts involved are first outlined and then studied more deeply within the framework of the following course activities:
- Lectures which elucidate and explain the mathematical theory
- Exercise sessions where related problems are solved individually or in groups.


To be announced.

Examination including compulsory elements

Written examination. Compulsory turn in assignments.

Page manager Published: Thu 04 Feb 2021.