High-dimensional bayesian classifiers using non-local priors

Common goals in classification problems are (i) obtaining predictions and (ii) identifying subsets of highly predictive variables. Bayesian classifiers quantify the uncertainty in all steps of the prediction. However, common Bayesian procedures can be slow in excluding features with no predictive power (Johnson & Rossell. (2010). In certain high-dimensional setups the posterior probability assigned to the correct set of predictors converges to 0 (Johnson and Rossell 2012). We study the use of non-local priors (NLP), which overcome the above mentioned limitations. We introduce a new family of NLP and derive efficient MCMC schemes. Springer International Publishing Switzerland 2013.

High-dimensional bayesian classifiers using non-local priors Conference Paper