This chapter deals with basic concepts and definitions concerning Monte Carlo sampling techniques. Rejection sampling and importance sampling are first introduced. Markov chains and some of their basic properties are discussed. Then the Metropolis-Hastings and Gibbs algorithms are presented. At the end of the chapter, a case study concerning change point detection is considered.