Approximate Bayesian Computation is a family of likelihood-free inference
techniques that are well-suited to models defined in terms of a stochastic
generating mechanism. In a nutshell, Approximate Bayesian Computation proceeds
by computing summary statistics ${\bf s}_{obs}$ from the data and simulating
synthetic summary statistics for different values of the parameter $\Theta$.
The posterior distribution is then approximated with an estimator of the
conditional density $g(\Theta|{\bf s}_{obs})$.