This unit presents basic ideas in the theory of random processes in discrete and continuous time, developed in parallel with a wide range of applications and examples. The core material is the theory of Markov chains in discrete and continuous time. Further material may include Poisson processes, birth–death processes, renewal process, stochastic simulation and Markov Chain Monte Carlo methods. Applications are drawn from bioinformatics, computer science, epidemiology, finance, genetics, image processing, operations research (inventory, reliability and queuing models) and spatial data. A mathematical package is used for effective computation with Markov chains. -- Course Website
Instructor: Associate Professor Gopalan Nair
Prerequisites: MATH2209 Calculus and Probability or STAT2226 Statistical Models for Data or STAT2402 Analysis of Observations or FINA2205 Quantitative Methods for Finance
