statistics G6102 Administrative Stuff

Instructor: David Madigan
email: madigan at stat dot etc.
phone: 212-851-2146
office hours: after class Mondays and Wednesdays

We will have weekly homework assignments and also a project.

• Introduction to the Bayesian approach
• First-cut Bayesian Computation
• Large-Sample Bayes
• Hierarchical Models
• Bayes Factors
• Monte Carlo
• MCMC
• Adaptive Rejection Sampling
• Probabilistic Graphical Models
• MCMC Diagnostics
• Sequential Monte Carlo
• Computing the Marginal Likelihood
• Case Study: Text Categorization
• Bayesian Hidden Markov Models
• Bayesian Causal Modeling

Here are some relevant books:

Bayesian Data Analysis (second edition), by Gelman, Meng, Stern, and Rubin (Chapman and Hall, 2003) is the most relevant text but Andrew Gelman's course this semester will probably cover more of the material in BDA than we will.

We will also make use of:

* B.P. Carlin and T.A. Louis (2000) Bayes and Empirical Bayes Methods for Data Analysis, Chapman and Hall.
* W.G. Gilks, S. Richardson, and D.J. Spiegelhalter (1995) Markov Chain Monte Carlo in Practice, CRC Press.
* P. Congdon (2001). Bayesian Statistical Modelling, Wiley.
* J. Gill (2002). Bayesian Methods. A Social and Behavioral Sciences Approach, Chapman and Hall.
* Michael Ross Chernick's list of Bayesian Books

Some more relevant books: