Abstarct: Approximate Bayesian Computation (ABC) methods, also known as likelihood-free sampling techniques, have been appeared in the past ten years as the most satisfactory approach to Bayesian analysis of complex and intractable likelihood problems. ABC was used first in genetics, then in a broader spectrum of applications. Over the past few years, both practical and theoretical issues of these methods, in statistics and other disciplines, are desired. Due to the massive and unprecedented reception of such methods, in this thesis, we present different algorithms and review some theoretical aspects for ABC methods. Then, we show some examples of their applications in real and simulated situations.