  This article presents a Bayesian inferential method where the likelihood for
a model is unknown but where data can easily be simulated from the model. We
discretize simulated (continuous) data to estimate the implicit likelihood in a
Bayesian analysis employing a Markov chain Monte Carlo algorithm. Three
examples are presented as well as a small study on some of the method's
properties.
