标题： 无似然性的预付参数估计 显示英文标题

标题： Prepaid parameter estimation without likelihoods

摘要： 在各个领域，感兴趣的统计模型在解析上通常是不可处理的。因此，计算约束极大地阻碍了统计推断。然而，对于一个给定的模型，不同的数据使用者可能执行相似的计算。一个使用者完成的计算对其他拥有不同数据集的使用者可能是潜在有用的。我们提议研究人员之间资源共享以利用这一点。更具体地说，我们在预付费数据库中预先绘制出所有可能的模型结果的空间。使用先进的插值技术，任何个体估计问题现在都可以在现场解决。预付费方法可以轻松适应不同的先验以及参数约束。我们为三个具有挑战性的模型创建了预付费数据库，并演示了它们如何通过在线参数估计服务进行分发。我们的方法在速度（提升了23,000到100,000倍）和准确性方面都优于最先进的估计技术，并能够处理之前几乎无法估计的模型。 显示英文摘要

摘要： In various fields, statistical models of interest are analytically intractable. As a result, statistical inference is greatly hampered by computational constraints. However, given a model, different users with different data are likely to perform similar computations. Computations done by one user are potentially useful for other users with different data sets. We propose a pooling of resources across researchers to capitalize on this. More specifically, we preemptively chart out the entire space of possible model outcomes in a prepaid database. Using advanced interpolation techniques, any individual estimation problem can now be solved on the spot. The prepaid method can easily accommodate different priors as well as constraints on the parameters. We created prepaid databases for three challenging models and demonstrate how they can be distributed through an online parameter estimation service. Our method outperforms state-of-the-art estimation techniques in both speed (with a 23,000 to 100,000-fold speed up) and accuracy, and is able to handle previously quasi inestimable models.