    Close Help Print  Course module: 202001344  202001344Probability Theory Course info   Course module 202001344
Credits (ECTS) 5
Course type Study Unit
Language of instruction English
Contact person dr.ir. W.R.W. Scheinhardt
E-mail w.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      Close Help Print   