**CS795/895 Stochastic Modeling with Applications**

**Fall 2011**

**Instructor:** Prof. Stephan Olariu

Phone: 683-3915

**Course Description:**

This graduate course, targeted at a Computer Science audience, proposes to take a look at fundamental issues in Queueing Theory in view of their applications, mostly to Computer Science. The emphasis will be on those queueing modeling techniques that are useful and applicable to the study of data networks and give an in-depth insight into the underlying principles of isolated queueing systems as well as queuing networks. Although a great deal of effort will be spent on discussing basic concepts, applications will be discussed through many examples and case studies (to be presented to the class by the participants). The material is grouped into a set of modules -- most of them still under construction at this time. It has become clear that the computer science graduate student needs a good dose of exposure to probabilistic thinking and stochastic processes relevant to CS applications. To make this course self-contained, the first part will be devoted to a quick review of basic concepts in probability theory and stochastic processes.

The course material is organized around the following broad themes:

- Review of elementary probability
- Random variables and their moments
- Basic stochastic processes
- Random walks
- Discrete-time Markov chains
- Continuous-time Markov chains

- Advanced stochastic processes
- Poisson processes
- Renewal processes
- Markovian processes
- Birth-death processes

**Prerequisites:** graduate standing in Computer Science,
Computer Engineering, Electrical Engineering or permission of the instructor

**Text:** Henk C. Tijms "A First Course in Stochastic Models", Wiley and Sons, 2003

**Grading Scheme:**

- Class presentation 40%
- Programming project 40%
- Class participation 20%

**Office Hours:** to be arranged

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