American Statistical Association
New York City
Metropolitan Area Chapter

Mailman School of Public Health
Columbia University
Department of Biostatistics Colloquium



Prof. Jun Liu
Department of Statistics
Harvard University


In a regression or classification problem, one often has many potential predictors (independent variables), and these predictors may interact with each other to exert non-additive effects. I will present a Bayesian approach to search for these interactions. We were motivated by the epistasis detection problem in population-based genetic association studies, i.e., to detect interactions among multiple genetic defects (mutations) that may be causal to a specific complex disease. Existing methods are either of low power or computationally infeasible when facing a large number of genetic markers, and sometimes also many quantitative traits. Aided with MCMC sampling techniques, our Bayesian method can efficiently detect interactions among many thousands of markers. We will discuss how to extend this method to deal with general classification problems. This can be viewed as an extension of the naive Bayes method.

Date: Thursday, April 24, 2008
Time: 4:00 - 5:00 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Judith Jansen Conference Room
4th Floor - Room 425
New York, New York


Refreshments will be served at 3:30 P.M. in the
Biostatistics Conference Room (R627).

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