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Discrete Stochastic Processes
The objective of this course is to help the reader to understand the concepts of probability theory and how they apply to the rigorous construction of the most fundamental classes of stochastic processes. More broadly, its goal is to introduce analytical knowledge required for: network resource management, network dimensioning, network planning, evaluation and performance analysis and etc.
- Introduction to probability
- Random Variables
- Discrete Time Markov Chains
- Poisson Process
- Continuous Time Markov Chains
- Introduction to Queuing Theory
Prerequisites:
Grading Policy:
Teacher Assistants:
Time:
Term:
Every fall semester
Grade:
Graduate