Applied Stochastic Processes

Requires an undergraduate course in differential equations and linear algebra; some knowledge of probability and programming experience is recommended. Modeling, analysis, and simulation of stochastic processes with an emphasis on applications from biology, physics, and data science. Intended for students both within math and adjacent fields with a quantitative background. Stochastic processes covered include: Poisson process, discrete and continuous time Markov chains, and Stochastic differential equations.