Spatial Bayesian variable selection with application to functional magnetic resonance imaging
by Mike Smith
Abstract: In this talk a procedure to undertake Bayesian variable selection and model averaging for a series of regressions that are located on a lattice is proposed. For those regressors which are in common in the regressions, we consider using an Ising prior to smooth spatially the indicator variables representing whether or not the variable is zero or non-zero in each regression. This smooths spatially the probabilities that each independent variable is non-zero in each regression, and indirectly smooths spatially the regression coefficients. It is discussed how single site or multi-site sampling schemes can be used to evaluate the joint posterior distribution. The approach is applied to the problem of functional magnetic resonance imaging in medical statistics, where massive datasets arise that need prompt processing.
For More Information: Dr Owen Jones O.D.Jones@ms.unimelb.edu.au