Abstract

Approximate Bayesian computation (ABC) [1,2] is a popular method for Bayesian inference involving an intractable, or expensive to evaluate, likelihood function, but where simulation from the model is easy. The method consists of defining an alternative likelihood function, which is also in general intractable, but naturally lends itself to pseudo-marginal computations [3], hence, making the approach of practical interest. The aim of this chapter is to show the connections of ABC Markov chain Monte Carlo with pseudo-marginal algorithms, review their existing theoretical results, and discuss how these can inform practice and hopefully lead to fruitful methodological developments.