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 Course module: 202001344
 202001344Probability Theory
 Course info
Course module202001344
Credits (ECTS)5
Course typeStudy Unit
Language of instructionEnglish
Contact persondr.ir. W.R.W. Scheinhardt
E-mailw.r.w.scheinhardt@utwente.nl
Lecturer(s)
 Lecturer dr. J. de Jong Examiner dr.ir. G. Meinsma Examiner dr.ir. W.R.W. Scheinhardt Contactperson for the course dr.ir. W.R.W. Scheinhardt Lecturer dr. J.B. Timmer
Starting block
 2A / 2B
RemarksPart of module 3 B-AM.
Minor students: please register for the minor!
Application procedureYou apply via OSIRIS Student
Registration using OSIRISYes
 Aims
 body { font-size: 9pt; font-family: Arial } table { font-size: 9pt; font-family: Arial } Describe and apply basic concepts of probability theory such as outcome, event, probability, conditional probability, independence, stochastic variable, distribution, distribution function, frequency function, density, expectation, moment, (co)variance, correlation coefficient and moment generating function. Adequately model chance events. Describe and use the most important discrete and continuous distributions and their properties. Describe and apply moment generating functions, the Markov and Chebyshev inequalities, the weak and strong laws of large numbers and the central limit theorem. Use the technique of conditioning for the computation of probabilities and expectations.
 Content
 body { font-size: 9pt; font-family: Arial } table { font-size: 9pt; font-family: Arial } This course presents a mathematical foundation for the concepts associated with uncertainty and events of chance. In particular, you learn how to model events of chance as stochastic variables and some basic techniques to analyse those models. We do this both for discrete (the outcome of throwing a dice) as well as continuous (the time it takes you to drive home) variables. We relate different stochastic variables through concepts such as correlation and independence.  We introduce the concepts of conditioning to model prior information. If we have stochastic variables, which are the sum of many independent stochastic variables, then we will show that in certain cases, we can simplify the model by using the laws of large numbers.
Assumed previous knowledge
 Basic Calculus skills, including Double integrals, Geometric series, Taylor series
 Module
 Module 3
 Participating study
 Bachelor Applied Mathematics
Required materials
Book
 S.M. Ross, Introduction to probability models, 9th International Student Edition, ISBN 9780123736352
Recommended materials
-
Instructional modes
Lecture

Practical
 Presence duty Yes

Self study without assistance

Tutorial

Tests
 Probability Theory
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