摘要：Approximate Bayesian Computation (ABC) is a popular computational method for likelihood-free Bayesian inference. The term “likelihood-free” refers to problems where the likelihood is intractable to compute or estimate directly, but where it is possible to generate simulated data $X$ relatively easily given a candidate set of parameters $\theta$ simulated from a prior distribution. Parameters which generate simulated data within some tolerance $\delta$ of the observed data $x^{*}$ are regarded as plausible, and a collection of such $\theta$ is used to estimate the posterior distribution $\theta |X=x^{*}$. Suitable choice of $\delta$ is vital for ABC methods to return good approximations to $\theta$ in reasonable computational time.
