CENG 381
Stochastic Processes
Probability spaces, random variables, distribution and density functions, random vectors, sequences of random variables, convergence notions, the central limit theorem, the law of large numbers, stochastic processes, stationary notions, Poisson processes, Gaussian processes, transformations of stochastic processes, ergodicity, second order random processes, representation theorems, Markov processes, homogeneous Markov models and applications.
| Topics |
| Introduction |
| Quick Review of Probability |
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Random Variables, Parameters of Random Variables, Jointly Distributed Random Variables
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Transform Methods, Moment Generating Function, Inequalities
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Probability Distributions – Discrete Random Variables
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Probability Distributions – Discrete RV s, Continuous RV s
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Probability Distributions – Continuous Random Variables – Central Limit Theorem, Applied Transforms
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| Midterm |
| Stochastic Processes, Poisson Process |
| Birth+Death Process |
| Markov Chains |
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Queueing Theory, Introduction, Describing a Queueing System
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| Little s Law, M/M/1 Queueing System |
| Other Queueing Systems |
