Statistics S626 

Contact:  Jianyu Wang jw257@indiana.edu 
Offered:  Fall, 2016 
Class Time:  11:1512:30 WH 005 
Class Days:  Tu, Th 
Capacity:  40 
Sequence:  No specific sequence. 
PreRequisites:  Two courses at the graduate level or consent by the instructor. A course equivalent to MATHM 463 (Introduction to Probability Theory) is ideal. 
Algebra Required:  Some preliminary knowledge of matrix algebra is needed to discuss some ideas about performing regression analysis from a Bayesian pointofview. 
Calculus Required:  Some notions of integration are needed. Specially dealing with integrals that arise from working with known probability distributions. Some basic knowledge of differentiation is needed too. 
Contact Person for Authorization:  Two statistics courses at the graduate level; otherwise, permission of instructor. 
Instructor:  Jianyu Wang 
Recommended followup classes:  Any topics course in advanced statistical methods that involve some form of Bayesian methodology. 
Syllabus:  No Syllabus Avaliable 
Keywords:  Prior and posterior distributions, Bayes theorem, model formulation, Bayesian computation, model checking and sensitivity analysis. 
Description:  The course covers an introduction to the theory and practice of Bayesian inference. Topics covered include: Prior and posterior distributions, Bayes theorem, model formulation, Bayesian computation, model checking and sensitivity analysis. This is a general class on Bayesian methods. Some basic knowledge of probability distributions, calculus and linear algebra is assumed. 
Books:  Required: *Hoff, Peter (2009) "A first Course in Bayesian Statistical Methods". New York: Springer. ISBN 9780387922997. (strongly) recommended: *Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., Rubin, D. B. (2003), Bayesian data analysis, Second Edition, Chapman and Hall/CRC. ISBN 9781439840955. *Marin, J. M. and Robert, C. (2007), Bayesian Core: A Practical Approach to Computational Bayesian Statistics. New York: Springer. ISBN 9780387389790. 
Substantive Orientation:  This course accommodates students from a variety of disciplines. In past semesters, S626 has been attended by students in Statistics, Computer Science, Economics, Biological Sciences, and Political Science, among others. 
Applied/Theoretical:  Historically this course had a theoretical focus. This semester we are pursuing more of a balance between theory and practice. 
Formal Computing Lab:  No 
Software:  R 
How Software is Used:  The software is mainly used for computation and data analysis for inclass examples and homework assignments. Only a reasonably low level of programming is required for both R and Winbugs. 
Problem Sets:  In the range of 56 homeworks a semester 
Data Analysis:  Yes, typically involving actual data sets. Examples of proportions, count data and estimation of rates are considered. Along with some regression models. 
Presentation:  No. 
Exams:  Historically, a midterm test and a final exam. 
Comments:  