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Stochastics 1

(course inactive since 31.03.2018)

Module name (EN):
Name of module in study programme. It should be precise and clear.
Stochastics 1
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Applied Informatics, Master, ASPO 01.10.2011
Module code: PIM-WI50
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P221-0145
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
2V (2 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
3
Semester: 1
Mandatory course: no
Language of instruction:
German
Assessment:
Written exam

[updated 20.12.2017]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

IngWi-Test Computer Science and Communication Systems, Master, ASPO 01.10.2017 , semester 1, optional course, not informatics specific, course inactive since 31.03.2018
PIM-WI50 (P221-0145) Applied Informatics, Master, ASPO 01.10.2011 , semester 1, optional course, not informatics specific, course inactive since 31.03.2018
IngWi-Test Applied Informatics, Master, ASPO 01.10.2017 , semester 1, optional course, not informatics specific, course inactive since 31.03.2018
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
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):
None.
Recommended as prerequisite for:
PIM-WI51 Stochastics 2
PIM-WI63


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


[updated 19.07.2007]
Lab:
Applied Mathematics, Statistics, and eLearning (5306)
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, WS 2016/17, WS 2015/16, WS 2014/15, WS 2013/14, ... 
[Tue Apr 16 18:52:56 CEST 2024, CKEY=ps1, BKEY=pim, CID=PIM-WI50, LANGUAGE=en, DATE=16.04.2024]