Population Stochastic approximation Monte Carlo Approximate Bayesian Computation

近似贝叶斯计算ABC方法的目的是模拟后验分布的似然函数是计算上难以处理的，并提供了一个样本可以实际上从抽样分布。几个常用的ABC版本，如ABC-MCMC，遭受局部捕获问题，在区域的低概率时，造成的距离函数的公差没有得到适当的调整。因此，在生成的样本和感兴趣的后验分布之间存在不期望的失配。本计画旨在利用随机近似蒙地卡罗的想法来克服这些问题，并利用族群的想法来增强所提出程序的稳定性。

Approximate Bayesian Computation ABC methods aim at simulating from posterior distributions whose likelihood function is computationally intractable and provided that a sample can be practically drawn from the sampling distribution. Several commonly used ABC versions such as ABC-MCMC, suffer from local trapping problems in regions of low probability which are caused when the tolerance in the distance function is not properly adjusted. As a result there is an undesirable mismatch between the generated sample and the posterior distribution of interest. This project aims at using Stochastic approximation Monte Carlo ideas to overcome such problems, and population ideas in order to enhance stability of the proposed procedure.