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"
agents; and inference from simulations.
- Venue: Tuesdays and Thurdays 12:00--1:20
Hall 208. Office hours in 229C Baker Hall, times to be determined.
Textbooks: Gary William
Computational Beauty of Nature,
and John Miller
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:
Corrupting the Young;
Enigmas of Chance
Posted at November 02, 2007 10:50 | permanent link