Applied Bayesian methods are an increasingly important tools in both industry and academia. We will start by understanding the basics of Bayesian methods and inference, what this is and how why it's important. This course is an introduction to Bayesian theory and methods, emphasizing both conceptual foundations and implementation. We will introduce the essential distinctions between classical and Bayesian methods and discuss the origins of Bayesian inference. After exploring the convenience of conjugate families of distributions, we will cover problems when the posterior is intractable. Topics include hierarchical and empirical Bayesian models, the foundations of subjective and objective priors, Bayesian credible intervals and hypothesis testing. Furthermore, we will concentrate on more advanced concepts such as how to evaluate Bayesian procedures, evaluating integrals that cannot be computed in closed form (Monte Carlo and MCMC). As part of the course we will learn tools that will aide us in Bayesian modeling and applied Bayesian methods such reproducible research through Markdown, RStudio. You will be responsible for learning these. You will be responsible for turn in reports that are well explained and well written (in additional to having code that is easily read and well documented). Failure to produce clear reports will result in deduction of points from all assignments.
Readings are posted at the top of each slide.
T/Th: 8:30--9:45 AM, Sociology Psychology 126
Labs (W): 11:45 -- 1:00 PM, Old Chem 101
Labs (W): 1:25 -- 2:40 PM, Old Chem 101
Labs (W): 3:00 -- 4:20 PM, Old Chem 101 You are not to switch labs for any reason.
Rebecca C. Steorts
Old Chemistry
beka [At] stat [dot] duke [dot] edu
Office: 218 Old Chemistry Hall
Office hours: Tuesday and Wednesday 10--11 AM.
Abbas Zaidi, PhD Student
abbas [dot] zaidi [AT] duke [dot] edu
Office hours: Thursday, 10-11 am, Old Chem 211A
Yikun (Joey) Zhou, MS Student
yikun[dot]zhou[at]duke.edu
Office hours: M,Tu,W, 2-3 pm, Old Chem 211A
Students are expected to have all course pre-reqs and be very familiar with R and will be expected to have learned LaTex/Markdown by the end of the course. All reports, exams, etc. should be submitted in Latex pdf format. All reports should be submitted in a format that is well explaned, with well documented and well written code. Please see Prof. Steorts if unsure whether you meet the requirements.
All homework's and take home exams \emph{must} be submitted to the Sakai website. You must submit them via the instructions on the homework or lab instructions and the format as well. Any failure to do so will result in deductions or a grade of 0. Submissions via email to the TA's or instructor will not be accepted for credit. See below for more information about LaTex/Markdown.
Excused absenses regarding exams must be approved BEFORE the time of the exam or within 48 hours after the exam (and you must have appropriate documentation). There will be no make up exams. If you do have an excused absence, your missed exam grade will be replaced by your final exam grade. Note that you cannot pass the course without taking the final exam. All work turned in for a grade must be entirely your own. This particularly relates to homework. You are encouraged to talk to each other regarding homework problems or to the instructor/TA, however the write up and solution \emph{must} be entirely your own solution and work.
Furthermore, you are responsible for everything from lecture. Do not depend on the course web page for announcements regarding due dates for homework, changes in schedules, etc. Such announcements will be made in class. Homework assignments will be uploaded to the course webpage along with course readings (please check here frequently for updates).
There is a Google Group course discussion page called dataMining521. Please direct questions about homeworks and other matters to that page. Otherwise, you can email the instructors (TAs and professor). Note that we are more likely to respond to the Google questions than to the email, and your classmates may respond too, so that is a good place to start.
Most questions should be directed to the Google group and Discussion Forum for the course. The webpage can be found at Multivariate Google Groups. Posting via email is done through: bbayes [at] googlegroups [dot] com.
Cell phones should be turned off (or set on silent). If you bring your laptops to class, please sit in the back so as not to distract others.
Please see the syllabus for other course policies.