36-667 Special Topics: Data over Space & Time

9 units, Carnegie Mellon University

This course is an introduction to the opportunities and challenges of analyzing data from processes unfolding over space and time. It will cover basic descriptive statistics for spatial and temporal patterns linear methods for interpolating, extrapolating, and smoothing spatiotemporal data basic nonlinear modeling and statistical inference with dependent observations. Class work will combine practical exercises in R, a little mathematics on the underlying theory, and case studies analyzing real problems from various fields economics, history, meteorology, ecology, etc.. Depending on available time and class interest, additional topics may include statistics of Markov and hiddenMarkov statespace models statistics of point processes simulation and simulationbased inference agentbased modeling dynamical systems theory.

Prerequisites: none listed

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