论文标题
快速推理的便宜的引导方法
A Cheap Bootstrap Method for Fast Inference
论文作者
论文摘要
Bootstrap是一种多功能推理方法，在许多统计问题中已证明有力。但是，当应用于现代大型模型时，它可能会面临重复的数据重新采样和模型拟合的大量计算需求。我们提出了一种使用最少计算的自举方法，即以一种蒙特卡洛复制的重新样本努力，同时保持理想的统计保证。我们介绍了这种方法的理论，该理论利用了标准引导原理的扭曲视角。我们还提出了这种方法嵌套采样问题和一系列亚采样变体的概括，并说明了如何将其用于跨不同估计问题的快速推断。
The bootstrap is a versatile inference method that has proven powerful in many statistical problems. However, when applied to modern large-scale models, it could face substantial computation demand from repeated data resampling and model fitting. We present a bootstrap methodology that uses minimal computation, namely with a resample effort as low as one Monte Carlo replication, while maintaining desirable statistical guarantees. We present the theory of this method that uses a twisted perspective from the standard bootstrap principle. We also present generalizations of this method to nested sampling problems and to a range of subsampling variants, and illustrate how it can be used for fast inference across different estimation problems.
