  Methods for Bayesian simulation in the presence of computationally
intractable likelihood functions are of growing interest. Termed
likelihood-free samplers, standard simulation algorithms such as Markov chain
Monte Carlo have been adapted for this setting. In this article, by presenting
generalisations of existing algorithms, we demonstrate that likelihood-free
samplers can be ambiguous over the form of the target distribution. We also
consider the theoretical justification of these samplers. Distinguishing
between the forms of the target distribution may have implications for the
future development of likelihood-free samplers.
