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Course prerequisite: ST521 and corequisite: ST740 Text:Lyle Broemeling (2007). Bayesian Biostatistics and Diagnostic Medicine. Chapman and Hall/CRC. (ISBN: 1584887672) Homework: Homework will normally be assigned (as indicated on the homework page) at the end of class on alternate Thursdays. Unexcused late homework will not be accepted. The final homework average will be computed after dropping the two lowest grades. Examinations: There will be no inclass examinations. Students are however required to submit two project works. Projects: Thre Project schedule:
Asking questions: If you have questions
about lectures, homework assignments, exams, procedures or any other aspect of
the course please log onto http://courses.ncsu.edu/st794/, and click on "Message
Board". Then click on "Post New Topic", enter your question
in the Message box, and click on "Submit Message". You will receive a
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Anonymous mail: If you wish to send me an anonymous suggestion or reminder, send email to st794001sup@wolfware.ncsu.edu. The system will remove mail headers, but you must remember to remove your signature and other identifying information. Grading System: Final grade will be based on: Final Semester Score = (2xHW + CP + 8xMP + 9xFP)/20 where HW is the homework average (out of 100) after dropping the two lowest scores, CP is based on class participation and MP and FP are the scores (out of 100) on the midterm and the final projects, respectively. Grades will be assigned on the +/ scale. Auditing: Auditors are expected to attend class regularly and submit homework on the same schedule as the other students. The final grade for auditors (AU or NR) will be based on their final homework average after dropping the two lowest scores. An average homework score of 75 or better is required for an AU. Policy on Academic Integrity: The University policy on academic integrity is spelled out in Code of Student Conduct. For a more though elaboration see the NCSU Office of Student Conduct website. For this course group work on homework is encouraged. However copying someone else's work and calling them your own is plagiarism, so the work you turn in should be your own. Students with Disabilities: Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with Disability Services for Students (DSS), 1900 Student Health Center, CB# 7509, 5157653. Reference material (Have requested these be on reserve at DH Hill Library): Berry, D. A. and Stangl, D. K. (1996). Bayesian Biostatistics, CRC Press, New York. Carlin, B. P. and Louis, T. A. (2008). Bayesian Methods for Data Analysis (Third Edition), CRC Press, New York. Marin, J. M. and Robert, C. P. (2007). Bayesian Core: A practical Approach to Computational Bayesian Statistics, Springer, New York. Moye, L. A. (2007). Elementary Bayesian Biostatistics, CRC Press, New York. 
Course objectives:
This course is an experimental offering focused on Bayesian inferential methods with emphasis on biostatistics applications. The essence of Bayesian methods is based on the concept of updating evidence using formal probabilistic rules. Unlike frequentist statistics, which attaches repeatedsampling frequencies to parameter estimators, Bayesian statistics directly describes uncertainty about unknown statistical parameters with a probability distribution. With this foundation, much of the Bayesian statistics follows from basic rules of probability theory and associated computational methods. In the past few years there have been a substantial change in attitudes of many biostatisticians and other applied statisticians toward implementation of the Bayesian paradigm. Recent developments of computational tools have brought Bayesian treatment of realistic, complex problems within the reach of practicing statisticians. Hence, knowledge of these techniques is critical for conducting research in most areas of contemporary biostatistics. This course will illustrate a variety of computational methods, simulation techniques, and hierarchical models suitable for analyzing clinical data. The primary emphasis will be on gaining an intuitive grasp of how the models work and what is needed to implement them for biostatistics research. Instead of spending much time on formal proofs, the course will use informal simulation experiments, case studies, and applied exercises to examine the intuition and working properties of the models. Using a wide variety of real data examples, this course will illustrate some key features of Bayesian inference, including the comparisons of means and proportions in the context of bioequivalence studies. A critical component of this course will be writing and running original codes using freely available software like R and WinBUGS. Students taking the course will have completed both ST520 and/or ST740. Syllabus: In ST790C we shall complete the following concepts.
Last updated on: Jan 08, 2009 