  Also known as likelihood-free methods, approximate Bayesian computational
(ABC) methods have appeared in the past ten years as the most satisfactory
approach to untractable likelihood problems, first in genetics then in a
broader spectrum of applications. However, these methods suffer to some degree
from calibration difficulties that make them rather volatile in their
implementation and thus render them suspicious to the users of more traditional
Monte Carlo methods. In this survey, we study the various improvements and
extensions made to the original ABC algorithm over the recent years.
