To 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.
This 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.
Combined lectures and tutorials.
1. Individual assignment. 2. Midterm exam. 3. Final exam.
Hamdy A. Taha: Operations Research, An Introduction. Tenth Edition.
Introductory courses on Probability Theory and Statistics
Courses in Mathematical Analysis, Discrete Mathematics, Linear Algebra.
Junior/Senior undergraduates in Applied Mathematics (e.g. Econometrics
and Operations Research)
The course is suitable to be taken in an exchange progam.