Bayesian methods for high-dimensional applications

This thesis deals with the introduction of novel Bayesian methods for the analysis of high-dimensional data. The methods assess the following problems: - variable selection when the enumartion of the space of competing models is prevented; - Bayesian network analysis for the estimation of multiple correlated precision matrices; - improving prediction and variable selection in a regression problem by exploiting prior external information on the covariates.