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Stochastics 2

(course inactive since 31.03.2018)

Module name (EN):
Name of module in study programme. It should be precise and clear.
Stochastics 2
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Applied Informatics, Master, ASPO 01.10.2011
Module code: PIM-WI51
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.
P221-0168
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: 2
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.

KIM-STO2 Computer Science and Communication Systems, Master, ASPO 01.10.2017 , semester 2, optional course, not informatics specific, course inactive since 31.03.2018
PIM-WI51 (P221-0168) Applied Informatics, Master, ASPO 01.10.2011 , semester 2, optional course, not informatics specific, course inactive since 31.03.2018
PIM-STO2 (P221-0192) Applied Informatics, Master, ASPO 01.10.2017 , semester 2, 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):
PIM-WI50 Stochastics 1


[updated 12.01.2018]
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Barbara Grabowski
Lecturer:
Prof. Dr. Barbara Grabowski


[updated 19.07.2007]
Lab:
Applied Mathematics, Statistics, and eLearning (5306)
Learning outcomes:
Based on Stochastics 1, this course will teach stochastic methods with a special focus on their applications in informatics. The lecture will focus on performance analysis methods (traffic theory) for discrete systems and the optimal coding of information.
After successfully completing this module, students will be able to estimate unknown probabilities and parameters such as expected values and variances based on observation data and calculate how large the number of observations should be in order to ensure that the estimates comply with a given accuracy and safety probability They will be able to establish hypotheses about unknown distribution types and their parameters and to test them with the correct statistical methods.
Students will be able to model complex discrete random systems using a simulation program and evaluate the simulation results statistically.   
  

[updated 24.02.2018]
Module content:
1.      Distributions of random variable functions
1.1     Limit theorems
 
2.      Statistical inferences
2.1     Sample size determination for estimating probabilities and averages
2.2     Tolerance intervals and hypothesis tests
2.3     Special hypothesis tests to determine distributions and compare probabilities
        and averages
 
3.      Special applications in Informatics      
3.1        Generation of random numbers
3.2        Application of statistical methods in the simulation of discrete systems
3.3     Queueing theory
3.4        Applications in traffic measurement
3.5     Statistical methods in information and coding theory


[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 the e-learning system OLAT:Statistik, R and ANYLOGIC.
Students will become familiar with the AnyLogic simulation program and complete their homework and exercises using the systems mentioned above.


[updated 30.07.2021]
Recommended or required reading:
KLIMANT, Herbert; PIOTRASCHKE, Rudi; SCHÖNFELD, Dagmar: Informations- und Kodierungstheorie, B.G.Teubner, Leipzig, 1996
WARMUTH, Elke: Mathematische Modelle in der Simulation diskreter Systeme, ZFH Koblenz, 2002.
GRABOWSKI, Barbara: Stochastik für Informatiker, e-Learning-Buch in OpenOLAT.


[updated 24.02.2018]
Module offered in:
SS 2017, SS 2016, SS 2015, SS 2014, SS 2013, ... 
[Fri Apr 26 13:12:58 CEST 2024, CKEY=ps2, BKEY=pim, CID=PIM-WI51, LANGUAGE=en, DATE=26.04.2024]