35363 Stochastic Models

Stochastic models allow many situations involving uncertainty to be analysed. This subject provides students with the knowledge required to use such models successfully in practice. Students acquire experience in using state-of-the-art commercial software for simulation, Markov decision process methods and various queuing models. Topics covered include Markov chains, Poisson processes, the birth-and-death process, and non-birth-and-death queuing models. The simulation component of the subject includes: pseudorandom number generation and corresponding statistical tests; evaluation of integrals using Monte Carlo simulation; generation of continuous and discrete random variables, including inverse transform technique, convolution method, and acceptance-rejection technique.

Typical availability

Autumn semester, City campus