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Modulbeschreibung

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Stochastics

Inhalt

  • Descriptive statistics

  • Probability theory, random variables

  • Discrete and continuous distributions

  • Inductive statistics: interval estimates

  • Markov chains and queuing

  • Simulation and MATLAB

Lernergebnisse

On completion of the module, the students will be able to:

Professional Competence

  • describe and interpret data by a few key indicators meaningfully

  • expect probabilities

  • apply the most important discrete and continuous distributions meaningfully

Methodological Competence

  • recognize the random component in abstract tasks and formulate in the language of the random variables

  • model stochastically and recognize tasks and

  • break complex textual problems into steps and solve exercise tasks

Social and Self-competence

  • support each other in solving tasks and in the context of self-learning units

  • assess their own skills in analysing problems and in developing solutions

ECTS

5 Punkte

Studien- und Prüfungsleistungen

Prüfungsleistungen:
  • Stochastik (90 min, Klausur)
Studienleistungen:
  • Stochastics (Hausarbeit)

Lehr- und Lernformen

  • Stochastics (3 SWS, Vorlesung)
  • Stochastics (1 SWS, Übung)

Studiengänge

  • Computer Science(CTS) - Pflichtmodul
  • Computer Science International Bachelor(ICS) - Pflichtmodul

Literatur

Peter Hartmann. Mathematik für Informatiker. Vieweg, 978-3834800961, 3 2006.
Gerhard Hübner. Stochastik. , 978-3834807175, 3 2009.
Michael Baron. Probability and Statistics for Computer Scientists. Chapman & Hall, 978-1584886419, 11 2006.
Ottmar Beucher. Wahrscheinlichkeitsrechnung und Statistik mit MATLAB. Springer, 978-3540721550, 9 2007.

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