Bayesian parameter inference consists in updating prior beliefs about the possible values of a model's parameters through the probability of reproducing the observed data with the model evaluated at each of these values (i.e., the likelihood function). For many models, an expression for the likelihood function is unknown and therefore many Bayesian methods are infeasible. Approximate Bayesian Computation (ABC) methods overcome this restriction by exploiting simulations from the model to approximate the unknown likelihood function. For this purpose, by expliciting what we consider a 'close enough' reproduction of the data in the simulations ABC can approximate the updated or posterior probability distribution of the model's parameters. This presentation will cover basic elements of Bayesian inference, an overview of common Markov Chain Monte Carlo (MCMC) sampling methods used for this purpose, and an introduction to ABC methods exploiting these notions with applications to common Econometric models.