Concentration inequalities have been recognized as fundamental tools in
several domains such as geometry of Banach spaces or random
combinatorics. They also turn to be essential tools to develop a non
asymptotic theory in statistics. This volume provides an overview of a
non asymptotic theory for model selection. It also discusses some
selected applications to variable selection, change points detection and
statistical learning.