Wolfe
Back Home Up Next

 

Cleveland
Friedman
Grunwald
Jewell
Kolaczyk
Lee, T.
Lee, Y. 
Madigan
Meng
Muthukrishnan
Nair
Nolan
Rus
Saul
Singer
Wainwright
Wolfe
Wu
Yu
A Bayesian View of Some Fundamental Problems in Statistical Signal 
Processing
Patrick J. Wolfe 
Harvard University
Many problems in science and engineering can be reduced in essence to the underlying statistical task of regression/classification. In this talk I will concentrate on the former aspect and outline some important problems in signal processing that I hope may be of interest to the statistical community. While engineering has traditionally borrowed from statistics, I hope to encourage more interaction by demonstrating some ways in which applications can serve to motivate new statistical theory and methodology. In particular, much recent work concerns itself with Bayesian probabilistic modeling. Regularization of some sort, the solution of ill-posed inverse problems, and optimal (or maybe just practical) estimation and decision-making under uncertainty are ubiquitous tasks. I will illustrate these types of problems with examples of my own research into the modeling of audio time series, in which Markov chain Monte Carlo methods are used to obtain point estimates for inference. This area provides a convenient test bed for more generally applicable techniques, and also serves to convey a sense of the requirements of typical signal processing problems.

Short Course: Information Theory & Statistics
Bin Yu & Mark Hansen
June 1, 2005
Colorado State University Campus
Fort Collins, CO 80523

Graybill Conference
June 2-3, 2005
Hilton Fort Collins

(Formerly: University Park Holiday- Inn)
Fort Collins, CO 80526

www.stat.colostate.edu/graybillconference
Graybill Conference Poster

Last Updated: Friday, May 24, 2005