Unbiased Monte Carlo for optimization and functions of expectations via multi-level randomization
Unbiased Monte Carlo for optimization and functions of expectations via multi-level randomization
复制标题
通过多级随机化进行无偏蒙特卡罗优化和期望函数
DOI：
10.1109/wsc.2015.7408524
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
P. Glynn
中科院分区：
文献类型：
--
作者：
J. Blanchet;P. Glynn
我们提出了设计和分析无偏蒙特卡罗估计量的一般原则，如α = g(E (X))，其中E (X)表示（可能是多维的）随机变量X的期望，g（·）是给定的确定性函数。在g（·）的局部二次可微性和适当的增长和有限矩假设等温和正则性条件下，我们的估计具有有限的工作归一化方差。我们将我们的估计器应用于各种感兴趣的设置，例如样本平均近似背景下的最优值估计，以及再生过程的无偏稳态模拟。其他应用包括粒子滤波器的无偏估计和条件期望。
We present general principles for the design and analysis of unbiased Monte Carlo estimators for quantities such as α = g(E (X)), where E (X) denotes the expectation of a (possibly multidimensional) random variable X, and g(·) is a given deterministic function. Our estimators possess finite work-normalized variance under mild regularity conditions such as local twice differentiability of g(·) and suitable growth and finite-moment assumptions. We apply our estimator to various settings of interest, such as optimal value estimation in the context of Sample Average Approximations, and unbiased steady-state simulation of regenerative processes. Other applications include unbiased estimators for particle filters and conditional expectations.
影响因子：
2.7
作者：
Giles, Michael B.
通讯作者：
Giles, Michael B.