Asymptotic properties of approximate Bayesian computation
研究了近似贝叶斯计算（ABC）方法所得后验分布的渐近行为，包括后验分布集中于真实参数的速度、极限形状以及后验均值的渐近分布，为实践者提供了理论指导。
Approximate Bayesian computation allows for statistical analysis using models with intractable likelihoods. In this paper we consider the asymptotic behaviour of the posterior distribution obtained by this method. We give general results on the rate at which the posterior distribution concentrates on sets containing the true parameter, the limiting shape of the posterior distribution, and the asymptotic distribution of the posterior mean. These results hold under given rates for the tolerance used within the method, mild regularity conditions on the summary statistics, and a condition linked to identification of the true parameters. Implications for practitioners are discussed.