Indiana University Bloomington

Statistics S626
Bayesian Theory And Data Analysis

Contact: Jianyu Wang jw257@indiana.edu
Offered: Fall, 2016
Class Time: 11:15-12:30 WH 005
Class Days: Tu, Th
Capacity: 40
Sequence: No specific sequence.
Pre-Requisites: Two courses at the graduate level or consent by the instructor. A course equivalent to MATH-M 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 point-of-view.
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 follow-up 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 978-0-387-92299-7.

(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 978-1-4398-4095-5.
*Marin, J. M. and Robert, C. (2007), Bayesian Core: A Practical Approach to Computational Bayesian Statistics. New York: Springer. ISBN 978-0-387-38979-0.
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 in-class examples and homework assignments. Only a reasonably low level of programming is required for both R and Winbugs.
Problem Sets: In the range of 5-6 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.
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