Stochastic Models
Many systems in Nature evolve randomly over time. For example, biological processes, chemical
processes, the stock market, and transportation and telecommunications systems. To control and optimize
various aspects of the system in question requires an accurate prediction of the future behaviour
of the system. Stochastic processes have been used as appropriate tools to model these
systems in modern science, technology and business. For example, they have been used in the prediction
of populations, in the control
of the spread of epidemics, in the management of investment portfolios, and in the dimensioning
of telecommunications networks.
This course is designed for anyone in mathematics, biological science, chemistry, computer science,
engineering, finance, physics, and other disciplines who is likely to encounter random processes
in their study or future work. Prerequisite for this course is Math 451 Probability Theory. Calculus and
linear algebra background is assumed.
This course will cover the following topics: Discrete Markov Chains, Poisson Processes,
Continuous-time Markov chains, random walks, and Brownian motion.
Stochastic models from a variety of fields will be considered.
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