36-724 Natural Science Applications of Stochastic Processes

6 units, Carnegie Mellon University

Much of the natural world is governed by the evolution of temporally and spatially dependent systems the study of stochastic processes provides statisticians tools to predict, succinctly explain, and quantify the uncertainty in these systems. This is a course in stochastic processes at the upper undergraduate and masters level. Basic theory will be covered, but the course focuses primarily on simulation and realworld, mostly temporallydependent data applications. Topics include finitestate Markov chains, branching processes, temporal and spatial Poisson processes. Case studies will be drawn from several fields, including neuroscience, epidemiology, seismology, molecular biology, and computer science. Familiarity with R is assumed.

Prerequisites: none listed

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