Insights into complex, high-dimensional data can be obtained by discovering features of the
data that match or do not match a model of interest. To formalize this task, we introduce the" …

High-dimensional data, where the number of features or covariates can even be larger than
the number of independent samples, are ubiquitous and are encountered on a regular basis …

__Abstract__ Advances in research technologies over the past few decades have
encouraged the proliferation of massive datasets, revolutionizing statistical perspectives on …

This thesis discusses Bayesian statistical inference in supervised learning problems where
the data are scarce but the number of features large. The focus is on two important tasks …

For high-dimensional data sets with complicated dependency structures, the full likelihood
approach often leads to intractable computational complexity. This imposes difficulty on …

This dissertation focuses on the choice of priors in Bayesian model selection and their
applied, theoretical and computational aspects. As George Box famously said “all models …

Standard assumptions incorporated into Bayesian model selection procedures result in
procedures that are not competitive with commonly used penalized likelihood methods. We …

We explore the theoretical and numerical properties of a fully Bayesian model selection
method in the context of sparse high-dimensional settings, ie, p≫ n, where p is the number …

For high-dimensional data, particularly when the number of predictors greatly exceeds the
sample size, selection of relevant predictors for regression is a challenging problem …

Model selection consistency in the high-dimensional regression setting can be achieved
only if strong assumptions are fulfilled. We therefore suggest to pursue a different goal …