Stochastic Processes

Catalog description

Stochastic modelling and analysis is key in understanding physical phenomena as well as designing new systems and quantifying various trade-offs and aspects of those designs. The course develops the foundations of stochastic processes and aims to provide engineering students with a mathematical, yet intuitive, toolbox to work with random processes. Topics covered include random walks, counting processes, renewal processes, Markov models and Markov decision processes, and martingales. Tools and techniques studied in this class are at the core of various fields ranging from engineering to social sciences and biology. Solid background in probability, preferably advanced probability, is required (e.g. ESE 3010 or equivalent). Some calculus and linear algebra will be needed (e.g. MATH 1040 and MATH 2400 )