Acceleration on Adaptive Importance Sampling with Sample Average Approximation
Acceleration on Adaptive Importance Sampling with Sample Average Approximation
复制标题
使用样本平均逼近的自适应重要性采样加速
DOI：
10.1137/15m1047192
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Ray Kawai
中科院分区：
文献类型：
--
作者：
Ray Kawai
我们构造和分析加速技术的自适应Monte Carlo模拟一般的多元概率定律时，样本平均近似采用最佳参数搜索。我们的目标是加速自适应蒙特卡罗估计的参数搜索线的基础上的样本平均近似，以达到一个接近最优的境界，在其小样本量阶段。首先，我们引入一个辅助参数的参数搜索线，并更新它明智的小样本收敛的目标。算法的所有三条线（蒙特卡罗平均，重要性采样参数搜索和辅助参数更新）以全自动的方式同时运行，具有一组公共的随机向量，而无需对仿真系统进行特别调整。接下来，我们提出并检查各种标准的辅助参数更新，所有的渐近正态估计所需的平均值和重要的抽样参数保持…
We construct and analyze acceleration techniques for adaptive Monte Carlo simulations for general multivariate probability laws when the sample average approximation is employed for optimal parameter search. Our goal is to accelerate the adaptive Monte Carlo estimation by leading the parameter search line based on the sample average approximation to reach a nearly optimal realm at its small sample size stage. First, we introduce an auxiliary parameter into the parameter search line and update it wisely to aim for small-sample convergence. All three lines of the algorithm (the Monte Carlo averaging, the importance sampling parameter search, and the auxiliary parameter updating) run concurrently in a fully automated manner with a common set of random vectors without ad hoc tuning of the simulation system. We next propose and examine various criteria for the auxiliary parameter updating, all under which the asymptotic normality of the estimator of the desired mean and of the importance sampling parameter hol...