January 08, 2009

Chaos, Complexity and Inference: 2009 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; this year, like last, the subject will be "chaos, complexity and inference". It worked pretty well last time, though I am not sure what to make of the fact that half of those currently registered are graduate students from other departments...

Anyone interested in the readings, assignments and notes can follow them either at the class syllabus page, or from this post and its RSS feed. (Last year's post.) — There have been some requests (as in, more than one!) for podcasts of the lectures. If someone will point me at an idiot's guide to setting one up, and tell me about cheap but adequate microphones, I'm willing to try.

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; social and other complex networks, and the analysis of network data; interacting agents; and inference from simulations.
Full Syllabus
At the course webpage, together with links to readings
Tuesdays and Thurdays 12:00--1:20 in Scaife Hall 208. Office hours in 229C Baker Hall, Wednesdays 10--11 and Thursdays 4--5.
Required Textbooks
Gary William Flake, The Computational Beauty of Nature
John Miller and Scott Page, Complex Adaptive Systems
Leonard Smith, Chaos: A Very Short Introduction
Optional Textbooks
Peter Guttorp, Stochastic Modeling of Scientific Data
Paul Krugman, The Self-Organizing Economy
Andrew M. Fraser, Hidden Markov Models and Dynamical Systems
W. John Braun and Duncan J. Murdoch, A First Course in Statistical Programming with R (Use of R is not required, but ask before using other languages.)
A previous course in mathematical statistics (such as 36-310, 36-401, or 36-625/626) and a course in probability including random processes (such as 36-217, 36-225/226, 36-410, or 36-625/626)
or consent of instructor.
(See the handout for more on required background.)
Some programming experience will be extremely helpful.

Corrupting the Young; Complexity; Enigmas of Chance

Posted at January 08, 2009 23:59 | permanent link

Three-Toed Sloth