November 02, 2007

Chaos, Complexity, and Inference (36-462): Course Announcement

I will be teaching 36-462, "topics in statistics", in the spring. This is a special topics course for advanced undergraduates, intended to expose them ideas they wouldn't see going through the ordinary curriculum.

36-462: Chaos, Complexity, and Inference
Description: This course will cover some key parts of modern theories of nonlinear dynamics ("chaos") and complex systems, and their connections to fundamental aspects of probability and statistics. By studying systems with many strongly-interacting components, students will learn how stochastic models can illuminate phenomena beyond the usual linear/Gaussian/independent realm, as well as gain a deeper understanding of why stochastic models work at all. Topics will include: chaos theory and nonlinear prediction; information; the distinction between randomness and determinism; self-organization and emergence; heavy-tailed and "scale-free" distributions; complex networks; interacting agents; and inference from simulations.
Venue: Tuesdays and Thurdays 12:00--1:20 in Scaife Hall 208. Office hours in 229C Baker Hall, times to be determined.
Required Textbooks: Gary William Flake, The Computational Beauty of Nature, and John Miller and Scott Page, Complex Adaptive Systems.
Optional Textbook: Peter Guttorp, Stochastic Modeling of Scientific Data.
Prerequisites: A previous course in mathematical statistics (such as 36-310, 36-401, or 36-625/626) and a course in probability and random processes (such as 36-217, 36-225/226, 36-410, or 36-625/626); or consent of instructor. Some programming experience will be helpful.

A more detailed syllabus will follow on the course website once I actually draw it up. If you have any questions, please send e-mail.

Update, 7 January 2008: Behold the syllabus.

Corrupting the Young; Complexity; Enigmas of Chance

Posted at November 02, 2007 10:50 | permanent link

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