Contents
GaTech Calendar
Syllabus/Calendar
Handouts
Quizzes/Exams/HW
Student Projects
Software Support
Programs/Codes
BayesLab
Data Page, Applications
Bayes Links
History
Philosophy
Bayesian Fun

BAYESIAN STATISTICS FOR ENGINEERS
Bayesian Inference, Bayesian Computation, Applications
 Philosophy

Bayes Stat makes its debut at GaTech in Fall 04.
This graduate course is concerned with Bayesian approach to statistical inference
for the analysis of data from a variety of applications. The orientation is
applied rather than theoretical, but such theory as is necessary for a proper understanding
of the Bayesian methodology will be covered.
Data from various scientific and engineering fields will be analysed during the course.
These will include: industrial experimentation, health systems, biomedical measurements, business forecasting,
signal processing, geosciences, environmental sciences, etc.
 Texts

 Christian Robert (2001) Bayesian Choice, 2nd Edition , Springer Verlag, NY.
 W. G. Gilks, S. Richardson, and D. J. Spiegelhalter (1995)
Markov Chain Monte Carlo in Practice, CRC Press.
 Christian Robert and George Casella (1999)
Monte Carlo Statistical Methods , Springer Verlag, NY.
 Jim Berger (1995) Statistical Decision Theory and
Bayesian Analysis, Second Edition, Springer Verlag, NY.
 Peter Congdon (2001) Bayesian Statistical Modelling, Wiley.
 Bernardo, J.M. and Smith, A.F.M. (1994) Bayesian Theory , Wiley.
 Agenda

 Why Bayes? Shortcomings and fallacies of classical statistics.
 "Why isn't everyone a Bayesian?"
 Bayesian Modeling: Prior, Posterior, Predictive Model
 Estimation and Testing. Bayes Factor. Credible sets.
 Bayesian Robustnes. Noninformative and automatic priors. Objective Bayes.
 Bayes on the Interface: GammaMinimax and Empirical Bayes
 Bayesian Computation. MC, Importance Sampling, MCMC, Particle Filters
 Software Support: Matlab, WinBUGS
 Bayesian Model and Variable Selection, Model Averaging
 Intro to Bayesian Nonparametric.
 Bayesian Networks
 Various Applications
