STOCHASTIC PROCESSES (4 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to: solve given probability problems using the concepts of Bayesian theory, Normal, Poisson, Exponential and Gamma distribution random variables; calculate limiting probabilities for discrete and continuous-time Markov chains in production process, birth and death process or other real phenomena; apply the concepts of Poisson process, renewal process, queuing or reliability theory to the given problem such as in engineering and operations research.

Topics:

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