MS8944 - Probability and Markov Chain Models | ||||||||
| ||||||||
| * The offering term is subject to change without prior notice | ||||||||
Course Aims | ||||||||
This advanced course aims to equip PhD students with a deep understanding of the theoretical foundations and practical applications of stochastic modeling and analysis. The curriculum will focus on the core topics of Markov chains and queuing systems, providing a solid grounding in the mathematical and statistical principles underpinning these stochastic processes. Through the use of two seminal textbooks, students will explore the formulation and solution of probability models, with an emphasis on their relevance to operations management and other real-world contexts. The course will delve into the properties and behavior of Markov chains, including stationary distributions, transient analysis, and the application of these concepts to various problem domains. Additionally, students will learn about queuing theory and its analytical techniques for modeling and optimizing service systems. By the end of the program, students will possess the knowledge and skills necessary to conduct advanced research, develop innovative stochastic models, and tackle challenging problems in their respective fields of study.? | ||||||||
Assessment (Indicative only, please check the detailed course information) | ||||||||
Continuous Assessment: 60% | ||||||||
Examination: 40% | ||||||||
Examination Duration: 2 hours | ||||||||
Detailed Course Information | ||||||||
| MS8944.pdf | ||||||||
