  This article surveys computational methods for posterior inference with
intractable likelihoods, that is where the likelihood function is unavailable
in closed form, or where evaluation of the likelihood is infeasible. We review
recent developments in pseudo-marginal methods, approximate Bayesian
computation (ABC), the exchange algorithm, thermodynamic integration, and
composite likelihood, paying particular attention to advancements in
scalability for large datasets. We also mention R and MATLAB source code for
implementations of these algorithms, where they are available.
