MS8944 - Probability and Markov Chain Models | ||||||||
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| * 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 | ||||||||
Useful Links | ||||||||
| Department of Management Sciences | ||||||||
