Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

Reproducing kernel Hilbert space (RKHS) is a popular tool used in machine learning and data mining. In this talk, we present a fully Bayesian framework and theory that coherently embed kernel regression/classification in a general nonparametric model. The theory behind our approach relates the model to statistical learning methods, showing the new class of priors supports the full range of functions in RKHS. Key practical features of our approach include the use of shrinkage priors to address problems of "large p", the use of mixture priors for feature selection, coherent updating as sample sizes change, and an understanding of so-called "unlabelled" data.

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.