A Variational Inference-Based Heteroscedastic Gaussian Process Approach for Simulation Metamodeling
A Variational Inference-Based Heteroscedastic Gaussian Process Approach for Simulation Metamodeling
复制标题
基于变分推理的异方差高斯过程仿真元建模方法
DOI：
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复制
发表时间：
2019
影响因子：
0.9
通讯作者：
Lin F. Yang
中科院分区：
文献类型：
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作者：
Wenjing Wang;Nan Chen;Xi Chen;Lin F. Yang
在这篇文章中，我们提出了一个变分贝叶斯推理为基础的高斯过程元建模方法（VBGP），适用于随机模拟实验的设计和分析。这种方法使统计和计算效率的近似的平均值和方差的响应面暗示的随机模拟，同时充分考虑到异方差的不确定性，此外，它可以适应的情况下，无论是一个或多个模拟复制可在每个设计点。通过两个数值例子，我们证明了VBGP与现有模拟元建模方法相比的上级性能。
In this article, we propose a variational Bayesian inference-based Gaussian process metamodeling approach (VBGP) that is suitable for the design and analysis of stochastic simulation experiments. This approach enables statistically and computationally efficient approximations to the mean and variance response surfaces implied by a stochastic simulation, while taking into full account the uncertainty in the heteroscedastic variance; furthermore, it can accommodate the situation where either one or multiple simulation replications are available at every design point. We demonstrate the superior performance of VBGP compared with existing simulation metamodeling methods through two numerical examples.