Contents
GaTech CalendarSyllabus/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. Non-informative and automatic priors. Objective Bayes.
- Bayes on the Interface: Gamma-Minimax 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