论文标题
带有抽样窗口的贝叶斯设计，用于复杂的空间过程
Bayesian Design with Sampling Windows for Complex Spatial Processes
论文作者
论文摘要
最佳设计有助于智能数据收集。在本文中，我们引入了一种具有复杂协方差结构的空间过程的全贝叶斯设计方法，例如自然生态系统中通常展示的方法。坐标交换算法通常用于查找最佳设计点。但是，在实践中通常是不可行的。当前，尚无规定可以选择设计的灵活性。我们还提出了一种方法，可以通过高斯工艺仿真找到贝叶斯采样窗口而不是点，以识别多维空间中高设计效率的区域。这些事态发展是由两个生态案例研究激发的：监测美国西北部河流网络系统的水温，并监测澳大利亚西北海岸附近淹没的珊瑚礁。
Optimal design facilitates intelligent data collection. In this paper, we introduce a fully Bayesian design approach for spatial processes with complex covariance structures, like those typically exhibited in natural ecosystems. Coordinate Exchange algorithms are commonly used to find optimal design points. However, collecting data at specific points is often infeasible in practice. Currently, there is no provision to allow for flexibility in the choice of design. We also propose an approach to find Bayesian sampling windows, rather than points, via Gaussian process emulation to identify regions of high design efficiency across a multi-dimensional space. These developments are motivated by two ecological case studies: monitoring water temperature in a river network system in the northwestern United States and monitoring submerged coral reefs off the north-west coast of Australia.
