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U of M PUBH 7440 - Syllabus

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1 Course Syllabus PubH 7440 Introduction to Bayesian Analysis Spring 2008 Credits: 3 Meeting Days: Tuesday-Thursday Meeting Time: 2:30-3:45pm Meeting Place: Mayo C381 (SPH Computer Lab) Instructors: Sudipto Banerjee and Bradley P. Carlin Teaching Asst: Laura Hatfield Office Address: A460 Mayo Bldg. MMC 303. 420 Delaware St SE. Minneapolis MN 55455 Office Phone: Banerjee: (612) 624-0624; Carlin: (612) 624-6646 Fax: (612) 626-0660 E-mails: [email protected]; [email protected]; [email protected] Office Hours: Sudipto: Mon 3:30-5:30pm or by appointment Brad: TuTh 3:45-5:15 pm or by appointment Laura (TA): TuTh 9:30-11:00 am (held in TA Room, Mayo A452) I. Course Description This course introduces hierarchical Bayesian statistical methods that enable investigators to combine information from similar experiments, account for complex spatial, temporal, and other correlations, and also incorporate prior information or expert knowledge (when available) into a statistical analysis. This course explains the theory behind Bayesian methods and their practical implementation, and also compares them with classical (frequentist) methods. The course emphasizes data analysis via modern computer simulation methods using WinBUGS and R (free statistical software) that are introduced and used in the course. II. Course Prerequisites Stat 5101-02 or PubH 7405-7406 or instructor's consent. If you are unsure about your qualifications for the course, please contact one of the instructors. III. Course Goals and Objectives Upon successful completion of the course, students will be able to independently formulate Bayesian hierarchical models for analyzing complex datasets arising from non-trivial statistical designs and experiments requiring such modeling. They will also be able to implement these models using statistical software, and write comprehensive reports for their analysis.2 IV. Methods of Instruction and Work Expectations Methods of instruction will be through in-class lectures and presentations, and also through hands-on practice with the WinBUGS and R software in the SPH Computer Lab (Mayo C381). V. Course Text and Readings The required text for the course will be the beta-test version of Bayesian Methods for Data Analysis, 3rd edition by Bradley P. Carlin and Thomas A. Louis. This will be passed out along with the syllabus on the first day of class at no cost to the student. VI. Course Outline/Weekly Schedule Week 1 (1/22): Preliminaries; Overview and basics of Bayesian Inference Week 2 (1/29): Introduction to the R computing environment and language; Basic Bayesian Computing Week 3 (2/5): Theory of Bayesian linear models; Bayesian linear models in R Week 4 (2/12): Introduction to WinBUGS and Hierarchical Modeling Week 5 (2/19): Bayesian Computing; Markov chain Monte Carlo (MCMC) methods; packages in R Week 6 (2/26): Review; First Midterm Exam (in-class) Week 7 (3/4): Bayesian Model Criticism and Selection Week 8 (3/11): Empirical Bayes methods: point and interval estimates, frequentist comparisons Week 9 (3/25): Bayesian designs for clinical trials Week 10 (4/1): Hierarchical longitudinal and time-series models Week 11 (4/8): Bayesian survival analysis and frailty models Week 12 (4/15): Review; Second Midterm Exam (take-home) Week 13 (4/22): Spatial and spatiotemporal models Week 14 (4/29): Bayesian nonparametric regression Week 15 (5/6): Bayesian inference for high-dimensional problems; case studies in Bayesian statistics VII. Evaluation and Grading Your final grade will be based upon homework assignments (30%), two midterms (15 and 25%, respectively), and a written final project at the end of the course (30%). For data analysis problems, your write-up must be a careful report of your models, methods, interpretations, and conclusions -- as if you were making a final report to a supervisor who has statistical training, but doesn't want to get bogged down in the details. Include the relevant parts of your computer output as a technical appendix, or ``cut and paste'' them into your report, labeling all plots, variables, and so forth. You need not get too carried away -- always substitute prose for output where possible. The final project involves preparing a short (say, 10-page) paper on some subtopic of interest to you. Once you have identified a topic of interest, we may suggest a paper or two for you to read as a starting point. This should in turn suggest several interesting project possibilities: extending an analytical result, simulating the performance of some procedure, undertaking a challenging data analysis, etc. We take a very dim view of unexcused late assignments, especially in a class like this where most of the work is ``take-home.'' As a general rule, prior notification is essential to our accepting a late paper of any kind. If illness or travel is going to cause you to miss a deadline, don't surprise us -- call or send an e-mail message (as crazed modern academics, we check our voice messages and e-mails constantly). Incomplete Grade A grade of incomplete “I” shall be assigned at the discretion of the instructor when, due to extraordinary circumstances, the student was prevented from completing the work of the course on time. The assignment of an incomplete requires a written agreement between the instructor and student specifying the time and3 manner in which the student will complete the course requirements. In no event may any such written agreement allow a period of longer than one year to complete the course requirements. University of Minnesota Uniform Grading and Transcript Policy A link to the policy can be found at onestop.umn.edu. VIII. Other Course Information and Policies Grade Option Change (if applicable) For full-semester courses, students may change their grad option, if applicable, through the second week of the semester. Grade option change deadlines for other terms (i.e. summer and half-semester) can be found at onestop.umn.edu. Course Withdrawal Students should refer to the Refund and Drop/Add Deadlines for the particular term at onestop.umn.edu for information and deadlines for withdrawing from a course. As a courtesy, students should notify their instructor and, if applicable, advisor of their intent to withdraw. Students wishing to withdraw from a course after the noted final deadline for a particular term must contact


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