Duration: 
  1 Semester |  Turnus of offer: 
  each winter semester |  Credit points: 
  5 |  
    Course of studies, specific field and terms: - Minor in Teaching Mathematics, Master of Education 2023 (compulsory), mathematics, 1st semester
 - Bachelor Computer Science 2019 (optional subject), Extended optional subjects, Arbitrary semester
 - Bachelor IT-Security 2016 (optional subject), mathematics, Arbitrary semester
 - Minor in Teaching Mathematics, Master of Education 2017 (compulsory), mathematics, 1st semester
 - Bachelor Computer Science 2016 (optional subject), advanced curriculum, Arbitrary semester
 - Bachelor CLS 2016 (compulsory), mathematics, 3rd semester
  
  |  
      Classes and lectures:   - Stochastics 2 (exercise, 2 SWS)
 - Stochastics 2 (lecture, 2 SWS)
     |   Workload:   - 60 Hours in-classroom work
 - 70 Hours private studies and exercises
 - 20 Hours exam preparation
     |       |  
      Contents of teaching:   |       - Lebesgue integral und Riemann integral
 - transformations of measures and integrals
 - product measures and Fubini's theorem
 - moments and dependency measures
 - normally distributed random vectors and distributions closely related to the normal distribution
     |       |  
   Qualification-goals/Competencies:   - Studends get insights into basic stochastic structures
 - They master techniques of integration being relevant to stochastics
 - They master the treatment of (particularly normally distributed) random vectors and their distributions
 - They are able to formalize complex stochastic problems
     |  
   Grading through:    |  
   Requires:    |  
    Responsible for this module:    Teachers:     |  
  Literature:  - J. Elstrodt: Maß- und Integrationstheorie - Springer
 - M. Fisz: Wahrscheinlichkeitsrechnung und mathematische Statistik - Deutscher Verlag der Wissenschaften
     |  
    Language:  |  
    Notes:Admission requirements for taking the module:  - None (the competencies of the modules listed under   |  
   Letzte Änderung: 1.2.2022  |  
 
 
	
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