The word stochastic is jargon for random. Many systems evolve over time with an inherent amount of randomness. A stochastic process is a system which evolves in time or space while undergoing chance fluctuations. We can describe such a system by defining a family of random variables. The objective of this course unit is to introduce the theory of stochastic processes, in particular Markov processes. The theory is illustrated with examples from operations research, biology, finance and economy. The study of probability models for stochastic processes involves a broad range of mathematical and computational tools. This course will strike a balance between the theory and the computing.
This course has 100 notional hours which includes approximately 30 hours of lectures and additional time spent by the student on self-learning, homework and assessments. For every one hour of lectures, a student is expected to devote at least 2 additional hours for studying.