Applied Probability and Stochastic Modeling

3 credits · Graduate credit

The bulk of this course focuses on stochastic models that capture the evolution in time of various random phenomena and/or dynamical systems. Such phenomena/systems arise extensively in diverse areas of research, ranging from biology, to data networks and production planning. Topics covered include Markov chains in discrete and continuous time, Poisson processes, Brownian motion, random walks, and their applications in key scientific and engineering areas. Additional topics in modern probability theory chosen by the instructor are covered in the last few weeks of the course.