Bayesian Methods for Prevention and Intervention Science
- James Allen, University of Minnesota Medical School, Duluth Campus
- David B. Henry, University of Illinois at Chicago
Bayesian statistics is often overlooked in the quantitative methods training for social and behavioral scientists. Typically, the only introduction that a student might have to Bayesian ideas is a brief overview of Bayes' theorem while studying probability in an introductory statistics class. This is not surprising. First, until recently, it was not feasible to conduct statistical modeling from a Bayesian perspective because of its complexity and lack of available software. Second, Bayesian statistics represents a powerful alternative to frequentist (classical) statistics, and is therefore, controversial. Recently, however, there has been great interest in the application of Bayesian statistical methods, mostly due to the availability of powerful statistical software tools that now make it possible to estimate simple or complex models from a Bayesian perspective.
The orientation of this workshop is to introduce prevention and intervention scientists to the basic elements of Bayesian statistics and to show, through discussion and practice, why the Bayesian perspective provides a powerful alternative to the frequentist perspective.
On Day 1 of the workshop we will explore the major differences between the Bayesian and frequentist paradigms of statistics, with particular focus on how uncertainty is characterized. The implications of the Bayesian perspective for hypothesis testing will be highlighted. Next, we will explore the basics of model building and model evaluation. On Day 2, we will discuss Bayesian computation focusing primarily on the R statistical computing environment. Also, on Day 2, participants will have time to run simple regression models using data from the SAFEChildren Study. On Day 3 we will consider somewhat more advanced models -- particularly growth curve models. The workshop will close with a discussion of the relative advantages of the Bayesian perspective, again leaving time for practice.
Prerequisites: Participants are expected to have a background in basic statistical methods up to, and including, regression analysis. Some exposure to growth curve modeling is desirable.
Application: Admission to this workshop is competitive. Enrollment is limited to 25 participants. Apply using the Summer Program portal (by clicking on the "Registration & Fees" tab at the top of this page) to provide your information, select the course, and complete the section on your quantitative/statistical experience. Also, upload the following documents via the portal:
- Current curriculum vita
- Cover letter summarizing research interests and experiences
Deadline: Deadline for application is May 1, 2014.
Fee: There are no tuition fees for accepted participants.