Course overview
This course introduces students to the fundamental concepts of random processes, particularly continuous-time Markov chains, and related structures. These are the essential building blocks of any random system, be it a telecommunications network, a hospital waiting list or a transport system. They also arise in many other environments, where you wish to capture the development of some element of random behaviour over time, such as the state of the surrounding environment.
Topics covered are: Continuous-time Markov-chains: definition and basic properties, transient behaviour, the stationary distribution, hitting probabilities and expected hitting times, reversibility; Queueing Networks: Kendall's notation, Jackson networks, mean; Loss Networks: truncated reversible processes, circuit-switched networks, reduced load approximations. Basic Queueing Theory: arrival processes, service time distributions, Little's Law; Point Processes: Poisson process, properties and generalisations; Renewal Processes: preliminaries, renewal function, renewal theory and applications, stationary and delayed renewal processes.
Course learning outcomes
- Demonstrate understanding of the mathematical basis of continuous-time Markov chains
- Demonstrate the ability to formulate continuous-time Markov chain models for relevant practical systems
- Demonstrate the ability to apply the theory developed in the course to problems of an appropriate level of difficulty
- Develop an appreciation of the role of random processes in system modelling
- Demonstrate skills in communicating mathematics orally and in writing