In complex models likelihood functions may be difficult to compute, or depend on assumptions about high order dependencies that may be difficult to verify. A number of methods have been devised to compute inference functions either meant to approximate the true likelihood function, or to provide inferential summaries that balance statistical efficiency with ease of computation. This talk will survey some of these approximations to likelihood and likelihood inference, with particular emphasis on recent developments in composite likelihood.