The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced. Typically a number of the initial states are attributed to "burn in" and removed, whilst the remainder of the chain is "thinned" if compression is also required. In this paper we consider the problem of retrospectively selecting a subset of states, of fixed cardinality, from the sample path such that the approximation provided by their empirical distribution is close to optimal. A novel method is proposed, based on greedy minimisation of a kernel Stein discrepancy, that is suitable for problems where heavy compression is required. Theoretical results guarantee consistency of the method and its effectiveness is demonstrated in the challenging context of parameter inference for ordinary differential equations. Software is available in the Stein Thinning package in Python, R and MATLAB.

翻译：使用超自然学来评估Markov 链条的趋同和压缩蒙特卡洛的输出量,从所产生的实证近似值来看,可能是次最佳的。 通常,一些最初的状态被归结为“ 烧伤” 并移除, 而如果也需要压缩,则链条的其余部分被“ 烧伤 ” 。 在本文中,我们考虑从抽样路径中追溯选择一组国家、 固定基点, 以便其经验分布提供的近似值接近于最佳水平的问题。 提出了一种新的方法, 其依据是贪婪地最小化内核石刻差异, 适合于需要重压缩的问题。 理论结果保证了方法及其有效性的一致性, 在普通差异方程式参数推断的富有挑战性背景中证明了这一点。 Python、 R 和 MATLAB 的Steinning 软件包中有软件。