Ricerc@Sapienza

We provide a review of prior distributions for objective Bayesian analysis. We start by examining some foundational issues and then organize our exposition into priors for: i) estimation or prediction; ii) model selection; iii) highdimensional models. With regard to i), we present some basic notions, and then move to more recent contributions on discrete parameter space, hierarchical models, nonparametric models, and penalizing complexity priors.

Classification methods, i.e., the chemometric strategies for predicting a qualitative response, find many applications in the omic sciences, where often data are collected in order to categorize individuals (e.g. according to whether they were treated or administered a placebo or, for instance, depending on if they were healthy or ill). After a brief discussion of the differences between discriminant and modelling approaches, some of the techniques most commonly used in the omic fields are illustrated in greater detail.

With the development of technology and the relatively higher availability of
new instrumentations, having multiblock data sets (eg, a set of samples analyzed
by different analytical techniques) is becoming more and more common
and, as a consequence, how to handle this kind of outcomes is a widely
discussed topic. In such a context, where the number of involved variables is
relatively high, selecting the most significant features is obviously relevant.
For this reason, the possibility of joining a multiblock regression method, the