论文标题
用于现实计算预算的多重蒙特卡洛法
A Multifidelity Monte Carlo Method for Realistic Computational Budgets
论文作者
论文摘要
提出了一种统计数量估计的多重型蒙特卡洛（MFMC）方法，该方法适用于任何规模的计算预算。基于一系列优化问题，每个优化问题都具有最小化的封闭形式解决方案，该方法扩展了众所周知的MFMC算法的可用性，在计算预算足够大的情况下将其恢复。理论结果验证了所提出的方法至少与其同名方法一样最佳，并保留了多因素估计的好处，其对预算或可用数据的假设最少，从而显着降低了简单的蒙特卡洛估计的差异。
A method for the multifidelity Monte Carlo (MFMC) estimation of statistical quantities is proposed which is applicable to computational budgets of any size. Based on a sequence of optimization problems each with a globally minimizing closed-form solution, this method extends the usability of a well known MFMC algorithm, recovering it when the computational budget is large enough. Theoretical results verify that the proposed approach is at least as optimal as its namesake and retains the benefits of multifidelity estimation with minimal assumptions on the budget or amount of available data, providing a notable reduction in variance over simple Monte Carlo estimation.
