Fall

IEORE4106 Stochastic Models


Instructor: David Yao

Additional Information: IEOR_E4106_Fall_2016_Syllabus.pdf

3 pts. Lect: 3. Probability and Statistics at the level of SIEO W3600 or IEOR E4150 or instructor permission. This is a required course for graduate students in Industrial Engineering, and Operations Research. This is also required for students in the Undergraduate Advanced Track. This course introduces students to operations research and stochastic processes. Operations research is concerned with quantitative decision problems, generally involving the allocation and control of limited resources, often in the presence of significant uncertainty. Stochastic processes are collections of random variables, usually indexed by time. [In stochastic process models, time can be regarded as either discrete or continuous.] For example, we might use stochastic processes to model the evolution of a stock price over time, the damage claims received by an insurance company over time, the work-in-process inventory in a factory over time or the number of calls waiting in a telephone call center over time, all of which evolve with considerable uncertainty. Among the stochastic processes to be considered are discrete-time Markov chains, random walks, continuous-time Markov chains, Poisson processes, birth-and-death processes, renewal processes, renewal-reward processes, Brownian motion and geometric Brownian motion. Among the engineering applications to be considered are queuing, inventory and finance.




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