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
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
Hall 208. Office hours in 229C Baker Hall, Wednesdays 10--11 and Thursdays 4--5.
- Gary William
Computational Beauty of Nature
- John Miller
- Leonard Smith, Chaos: A Very Short Introduction
Modeling of Scientific Data
- Paul Krugman, The
- 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;
Enigmas of Chance
Posted at January 08, 2009 23:59 | permanent link