620-301 Stochastic Modelling

Credit cannot be gained for both 620-301 and 300-331.

One of 620-201 or [01]620-203; and at least one of [01]620-112, 620-113, 620-122, 620-123, [98]620-130, [98]620-132, 620-142, 620-143, [99]620-200, 620-211.

This subject introduces the concept of a stochastic process and deals with the important standard stochastic processes, including Poisson process, Markov chains in discrete and continuous time (with some applications), renewal processes and time series. Students learn to understand, derive the behaviour and properties, and simulate simple stochastic process models derived from real-life situations. This subject demonstrates the importance of such models and in particular shows their applications to industry and the sciences.

Topics covered include review of the main concepts from probability theory, elements of utility theory, basic limit theorems, type of stochastic processes; analysis of Markov chains and their applications (including elements of Markov decision processes); random walks; the Poisson and general jump Markov processes and their applications (with elements of queueing models); renewal theory; elements of time series (stationarity, filtering, basic linear models, identification and estimation); and elements of simulation.

This subject contributes to developing students' generic skills as well. These include the ability to adopt new ideas, from participation in the lecture program, and to develop creative ways of solving unfamiliar problems, especially through practice classes.

Up to 50 pages of written assignments (20%); and a 3-hour end-of-semester written examination (80%).