Population Stochastic approximation Monte Carlo Approximate Bayesian Computation

近似贝叶斯计算ABC方法的目的是从后验分布进行模拟，其似然函数在计算上是困难的，并且假设可以从抽样分布实际提取样本。几个常用的ABC版本，如ABC-MCMC，在低概率区域遭遇局部陷阱问题，这是由于没有适当地调整距离函数中的容差而引起的。结果，在生成的样本和感兴趣的后验分布之间存在不希望的失配。本项目旨在利用随机逼近蒙特卡罗思想来克服这类问题，并用种群思想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.