标题： 近似贝叶斯计算中的统计摘要 显示英文标题

标题： Summary Statistics in Approximate Bayesian Computation

摘要： 本文件将作为即将出版的《近似贝叶斯计算（ABC）手册》的一章发表，该手册由S. Sisson、Y. Fan和M. Beaumont编辑。自最早关于ABC的研究以来，人们已经认识到使用摘要统计量对于产生有用推断结果至关重要。这是因为ABC会受到维度灾难的影响，使用高维输入会导致输出中的大逼近误差。因此，找到关于当前参数推断或模型选择任务的信息性低维摘要至关重要。本章回顾了已提出的选取此类摘要的方法，并在Blum等人（2013）之前的综述论文基础上进行了扩展，介绍了最近的发展。还讨论了与ABC维度灾难和充分性相关的理论结果。 显示英文摘要

摘要： This document is due to appear as a chapter of the forthcoming Handbook of Approximate Bayesian Computation (ABC) edited by S. Sisson, Y. Fan, and M. Beaumont. Since the earliest work on ABC, it has been recognised that using summary statistics is essential to produce useful inference results. This is because ABC suffers from a curse of dimensionality effect, whereby using high dimensional inputs causes large approximation errors in the output. It is therefore crucial to find low dimensional summaries which are informative about the parameter inference or model choice task at hand. This chapter reviews the methods which have been proposed to select such summaries, extending the previous review paper of Blum et al. (2013) with recent developments. Related theoretical results on the ABC curse of dimensionality and sufficiency are also discussed.