STOCHASTIC PROCESS (4 Credits)

Learning Outcomes:

On successful completion of this course, student will be able to: Apply probability concept to solve Bayesian problem; Calculate the important concept of random variables with Poisson, Exponential and Gamma distributions; Calculate limiting probabilities for Discrete and Continuous Times Markov Chain in production process, birth and death process or other real phenomen; Apply the important concept of Poisson process, Interarrival and Waiting time distribution; Apply Renewal, Queuing and Reliability theory in production process and network of queues.

Topics:

1. Probability and Random Variables;
2. Discrete-Time Markov Chains;
3. Poisson Process;
4. Continuous-Time Markov Chains;
5. Renewal Process;
6. Queuing Theory;
7. Reliability Theory.