论文标题
有限单调函数的集成：重新审视非顺序情况，重点是公正的蒙特卡洛（随机）方法
Integration of bounded monotone functions: Revisiting the nonsequential case, with a focus on unbiased Monte Carlo (randomized) methods
论文作者
论文摘要
在本文中，我们重新审视单调有限函数的数值集成问题，重点是非序列蒙特卡洛方法的类别。我们首先在非序列算法的最大$ l^p $错误上提供新的A下限，当p> 1> 1时，我们对Novak定理进行了改进。然后，我们专注于Case P = 2并研究了两种无偏见方法的最大误差，即一种基于控制差异技术的方法，以及分层的采样方法。
In this article we revisit the problem of numerical integration for monotone bounded functions, with a focus on the class of nonsequential Monte Carlo methods. We first provide new a lower bound on the maximal $L^p$ error of nonsequential algorithms, improving upon a theorem of Novak when p > 1. Then we concentrate on the case p = 2 and study the maximal error of two unbiased methods-namely, a method based on the control variate technique, and the stratified sampling method.
