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Stochastics 1

Module name (EN): Stochastics 1
Degree programme: Computer Science and Communication Systems, Master, ASPO 01.10.2017
Module code: KIM-STO1
Hours per semester week / Teaching method: 2V (2 hours per week)
ECTS credits: 3
Semester: 1
Mandatory course: no
Language of instruction:
Written exam

[updated 20.12.2017]
Applicability / Curricular relevance:
KIM-STO1 Computer Science and Communication Systems, Master, ASPO 01.10.2017, semester 1, optional course, not informatics specific
PIM-WI50 Applied Informatics, Master, ASPO 01.10.2011, semester 1, optional course, not informatics specific
PIM-STO1 Applied Informatics, Master, ASPO 01.10.2017, semester 1, optional course, not informatics specific
30 class hours (= 22.5 clock hours) over a 15-week period.
The total student study time is 90 hours (equivalent to 3 ECTS credits).
There are therefore 67.5 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
Recommended as prerequisite for:
KIM-STO2 Stochastics 2

[updated 12.01.2018]
Module coordinator:
Prof. Dr. Barbara Grabowski
Lecturer: Prof. Dr. Barbara Grabowski

[updated 10.11.2016]
Learning outcomes:
After successfully completing this module, students will be able to correctly select and apply statistical methods for the description of random data sets and the recognition of correlations and structures in these data sets, as well as to interpret the results of the analyses correctly
They will be able to describe random characteristics by probability distributions and know how to determine these distributions in practice. Students will be capable of calculating and interpreting probabilities.
They will be able to discretely calculate and analyze time-dependent random processes with finite state space using Markow models (chains) and the performance of systems that can be described by Markov chains.

[updated 24.02.2018]
Module content:
1.      Statistical basics for the analysis of large amounts of data
1.1     Statistical measures to describe correlations
1.2     Clustering methods
1.3     Classification
2.      Principles of probability calculus
3.      Markov chains and their applications
3.1     Discrete random variables
3.2     Markov chains
3.3     The usage of Markov chains in source coding
3.4     The usage of Markov chains in the simulation of discrete systems
4.      Random variables and their distributions
4.1     Discrete and continuous random variables
4.2     Special probability distributions and applications

[updated 24.02.2018]
Teaching methods/Media:
50% of the lecture will take place in the PC lab AMSEL "Angewandte Mathematik, Statistik und eLearning". Computer-supported practical case studies will be carried out here using R and ANYLOGIC.
In addition, the eLearning system MathCoach-Statistik (AMSEL PC laboratory 5306) will be used. Students must complete homework and exercises using this system.

[updated 24.02.2018]
Recommended or required reading:
MATHAR, Rudolf; PFEIFER, Dietmar: Stochastik für Informatiker, B.G.Teubner Stuttgart 1990.
GRABOWSKI, Barbara: Stochastik für Informatiker, e-Learning-Buch in OpenOLAT.

[updated 24.02.2018]
Module offered in:
WS 2017/18
[Sat Nov 27 04:16:58 CET 2021, CKEY=ps1, BKEY=kim2, CID=KIM-STO1, LANGUAGE=en, DATE=27.11.2021]