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UI STAT 4520 - Bayesian Statistics

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Bayesian Statistics, 22S:138Fall 20081 General InformationInstructor: Kate Cowles, 374 SH, [email protected] hours: T 1:30 - 2:20 p.m.W 12:30 - 1:20 p.m.Th 1:30 - 2:20 p.m.Please feel free to make appointments to see me outside of office hour s ,and to send me questions by e-mail.Department: Statistics and Actuarial Science, 241 SHDEO: Luke Tierney, 241 SH, 335-0712Lectures: M, W, F 11:30 - 12:20 66 SHLab: Alternate F 11:30 - 12:20 41 SHWeb page: http://www.stat.uiowa.edu/~kcowles/s138_2008Handouts, homework assignments, datasets, etc.will be posted on the web page for you to download.Textbook: Gelman, Carlin, S tern, and Rubin, Bayesian Data Analysis2nd ed., C hapman & Hall/CRC, 2004On reserve: Berry, Statistics: A Bayesian PerspectiveMath Sciences Library 125 MacLean Hall Johnson and Albert, Ordinal Data Modelinghttp://libres.lib.uiowa.edu/math/ Lee, Bayesian Statistics: An IntroductionCarlin and Louis, Bayes and Empirical Bayes Methods for Data AnalysisWinBUGS manuals2 Course goals and objectivesThrough hands-on experience with real data from a variety of applications, studentswill learn the basics of designing and carrying out Bayesian analyses, and interpretingand communicating t he results. Students will learn to use software packages includingWinBUGS and BOA to fit Bayesian models.13 Evaluation of students3.1 HomeworkHomework assignments will consist of data analysis on the computer, written inter-pretation of computer output, and other written questions. In general, homeworkwill be assigned each Fri. and will be due in class the following Fri. Exceptions tothis schedule will be announced in class.Show your work when solving written homework problems. For computer problems,turn in printouts of your commands o r programs and their output.You are encouraged to study with others. However, if you do work with others onhomework assignments, please: a) write up your own a ssignment and make sure youcompletely understand all solutions that you submit, and b) write the names of theothers in your study g roup on your assignment.Late homework will not be accepted except as required by university policy, i.e. be-cause of “illness, mandatory religious obligatio ns, or other unavoidable circumstancesor University activities.”3.2 ProjectsStudents will work in groups of three to carry out projects involving application ofBayesian methods to problems of their own choosing. Some examples are:• Carry out a complete Bayesian analysis of a real dataset. This might involve:– description of the research question and dataset– specifying an appropriate Bayesian model– determining appropriate values for prior par ameters– fitting the model using WinBUGS– checking convergence– analyzing the output using BOA– reporting and interpreting the results• Compare different methods of fitting the same model to the same dataset– normal approximations– MCMC– other simulation methods– analytical computation (if feasible)– etc.2• Carry out a Bayesian analysis of a dataset for which a classical analysis has beenreported in a journal. Compare and contrast the results obtained by the twoapproaches.• Fit a Bayesian model to a dataset using several different choices of prior (hy-perparameters and/or functional form). Discuss the meaning of the differentresults, and the robustness of the model to prior specifications.• Fit several different plausible Bayesian models to the same dataset. Carry outa check of model adequacy and model fit. Discuss the results.• There are endless other possibilities. Find something that interests you,or seeme for ideas.I will expect more sophisticated projects from graduate students.Projects will be carried out in three phases. Please meet with me at least once whileyou are working on each phase.• Project proposal (due 11 /03)This is a detailed description of what you plan to do, including question(s) t o beaddressed, dataset to be used, methods to be applied. Also specify the methodof presentation that you intend for the final project. (See below.)• Project interim r eport (due 11/19)This info rmal report will indicate that your project is “on track.” All computingshould be done a t this time. The report will include results obtained thus far anda brief summary (hand-written is O.K.) of what they mean and what remainsto be done. In addition, the report will include a list of the tasks performed byeach member of the project team.• Project presentation (papers or presentation materials must be posted or sub-mitted by 12/08)Projects must be finalized in a form that can be shared with the entire class,such as:– posting a document on the course web page– preparing a poster– giving an oral presentation with overheads, slides, o r computer imagesPosters and oral presentations will be given in class during the final week ofclasses.33.3 ExamsThere will be two 1-hour midterm exams and one comprehensive 2-hour final. Stu-dents may bring one 8-1/2 x 11 in. sheet of paper with notes to each midterm, and4 sheets to the final.Missed exams may be made up only with documentation of reasons required byuniversity policy (see “Late Homework” above).Exam dates and times:Midterm 1 Fri. 10/03 in classMidterm 2 Fri. 10/31 in classFinal Tues. 12/16 12:003.4 GradingThe course components will be weighted as follows:Homework 10%Midterms 35% (17.5% each)Project 20%Final 35%Grading will be on a curve, with +/− grades used. A grade of A+ represents excep-tional work and rarely is awarded.4 Extra HelpThe Statistics Tutorial Lab, located in 202 CC, gives free tutorial assistance to stu-dents in 22S:2, 8, 25, and 39. In addition, several graduate students have volunteeredto independently tutor students in various 22S: courses at mutually- arranged timesand fees. Please check the web site www.stat.uiowa.edu/courses/tutoring.html fortutoring details.5 College of Liberal Arts and Sciences: Policies and Proce-dures5.1 Administrative Home of the CourseThe administrative home of this course is the College of Liberal Arts and Sciences,which governs a cademic matters relating to the course such as the add/drop dead-lines, the second-grade-only option, issues concerning academic fraud or academic4probation, a nd how credits are applied for various graduation requirements. Differ-ent colleges might have different policies. If you have questions about these or otherCLAS policies, visit your academic advisor or 120 Schaeffer Hall and speak withthe


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