Course ObjectiveTo be introduced to the theory of stochastic processes and models that
are important in EOR practice. To learn modeling techniques for
translating an EOR problem into an appropriate stochastic model. To
learn how to apply optimization and simulation techniques for
performance analysis of stochastic systems.
Course ContentThis is an introductory course in stochastic models. It builds upon the
basic course in probability theory and extends the theory of static
probability to dynamic stochastic processes. The course focuses on
Poisson process, discrete-time and continuous-time Markov chains, with
applications to queueing models, risk analysis, reliability problems,
and option pricing. It also discusses dynamic optimization and
stochastic simulation of these systems.
Teaching MethodsCombined lectures and tutorials.
Method of Assessment1. Individual assignment. 2. Midterm exam. 3. Final exam.
LiteratureHamdy A. Taha: Operations Research, An Introduction. Tenth Edition.
Target AudienceJunior/Senior undergraduates in Applied Mathematics (e.g. Econometrics
and Operations Research)
Additional InformationThe course is suitable to be taken in an exchange progam.
Recommended background knowledgeIntroductory courses on Probability Theory and Statistics. Courses in
Mathematical Analysis, Discrete Mathematics, Linear Algebra.
|Language of Tuition||English|
|Faculty||School of Business and Economics|
|Course Coordinator||dr. A.A.N. Ridder|
|Examiner||dr. A.A.N. Ridder|
dr. A.A.N. Ridder
dr. D.A. van der Laan
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Lecture, Study Group|
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