The 21st century has seen an enormous growth in the development and use of approximate Bayesian methods. Such methods produce computational solutions to certain intractable statistical problems that challenge exact methods like Markov chain Monte Carlo: for instance, models with unavailable likelihoods, high-dimensional models, and models featuring large data sets. These approximate methods are the subject of this review. The aim is to help new researchers in particular -- and more generally those interested in adopting a Bayesian approach to empirical work -- distinguish between different approximate techniques; understand the sense in which they are approximate; appreciate when and why particular methods are useful; and see the ways in which they can can be combined.

翻译：21世纪,近似贝叶斯方法的开发和使用有了巨大增长,这些方法为某些棘手的统计问题提供了计算解决方案,这些问题挑战了马可夫链蒙特卡洛等精确方法:例如,没有可能性的模型、高维模型和大型数据集模型。这些近似方法是本审查的主题。目的是帮助新的研究人员 -- -- 更一般地说,帮助那些对采用巴耶斯方法的经验工作感兴趣的研究人员 -- -- 区分不同的近似技术;理解其近似含义;理解何时和为什么特定方法有用;并了解如何将它们结合起来。