Instructor:           Mark E. Glickman
               Boston University School of Public Health 

This Boston Chapter of the American Statistical Association Event is generously hosted by Mathworks.

Time: 8:00 AM Registration
          8:30 AM to 1:00PM course with 10:30 to 11:00 break.
 
Abstract: 
Most statisticians' initial exposure to statistics is from the 
classical or "Frequentist" point of view.  This comes as no 
surprise, as most statistical software encourages the nearly-exclusive 
use of classical statistical methods, and scientific journals 
tend to be much more accepting of the results of classical 
analyses.  In contrast, Bayesian statistics is often treated 
like a footnote in an otherwise lengthy body of statistical 
knowledge.  Relatively few classically-trained statisticians 
appreciate, for example, that Bayesian statistics is not a 
collection of special tools or clever statistical models and 
procedures, but is a competing framework for statistical 
inference that is self-consistent, does not require inventing 
new methods when one encounters a difficult problem, 
and enables a statistician to prioritize thinking critically and 
scientifically about constructing appropriate data models 
rather than focus on the procedures to analyze the data. 
 
This short course is aimed at practicing statisticians who are 
comfortable using classical approaches to data modeling and analysis, 
but are curious to learn more about the details of Bayesian statistics. 
The course is equally appropriate as a refresher in Bayesian methods. 
No previous knowledge of Bayesian statistics is assumed.  Topics 
will include: the philosophical underpinnings of Bayesian 
statistics; Bayes rule; prior and posterior distributions; choice of 
a prior distribution; predictive distributions; summarizing inferences; 
sequential updating; model selection and Bayes factors; and basic 
applications.  Modern computational tools including Monte Carlo 
Markov Chain simulation will be introduced.  The course will emphasize 
how to think from the point of view of a Bayesian statistician. 
 
Prerequisite:  
Familiarity with classical statistics and basic probability.

Directions:
http://www.mathworks.com/company/aboutus/directions.html

There is construction going on, so here are a few more details in case it looks confusing.

As you approach MathWorks on Route 9 eastbound, you'll pass construction and a giant pile of dirt. Enter the driveway after the dirt pile and before the building. Follow the driveway around the building, driving across the front (Route 9) side and then turning right. We'll meet in the building that is on your right as you face the garage entrance. You can park in the garage or anywhere else in the parking lot.

The building will be open at 8:00 for check-in, with the course scheduled to start at 8:30. There will be some refreshments at check-in and at the break, and lunch after the course.
 

To register by check, include the check made payable to BCASA, your name, affiliation, and mail by October 23 to:

Huichao Chen, PhD
Department of Biostatistics/CBAR
Harvard University
651 Huntington Ave, FXB502
Boston, MA 02115