STA 122 Bayesian and Modern Statistics

STA122: Bayesian and Modern Statistics

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Instructor:	Merlise Clyde
Email:	clyde@stat.duke.edu
Office:	223E Old Chemistry Building
Office Hours:	Mon 11-12 or by appointment

Teaching Assistant	Debdeep Pati
Email:	db55@stat.duke.edu
Office Hours:	211A Old Chem, see SECC Schedule for times

Lecture: M W 2:50 -4:05 SocSci 111
Lab: Tu: 1:15-2:30 Old Chem 01

Course Description: Principles of data analysis and advanced statistical modeling. Bayesian inference, prior and posterior distributions, hierarchical models, model checking and selection, missing data, stochastic simulation by Markov Chain Monte Carlo using WinBugs and R.

Prerequisites: Statistics 104, Statistics 114, and Statistics 121/Economics 139D or the equivalent.

Texts:

Bayesian Statistics: An Introduction (2004) Peter M. Lee (3rd Edition, Publisher Arnold, ISBN 13 9780340814055)
An Introduction to Generalized Linear Models (2008) Annette J Dobson and Adrian G. Barnett (3rd Edition, Publisher CRC Press, ISBN 978-1-58488-9502)

Grading: Course Grades will be based on a Class Project & Presentation.(30%), Midterm (30%) and Homework (40%). Students are expected to participate in class discussions based on readings and assignments.

Updated 1/6/2008