Data Page, Applications
BAYESIAN STATISTICS FOR ENGINEERS
Bayesian Inference, Bayesian Computation, Applications
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.
- 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.
- 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