论文标题
地球物理参数检索的深层高斯工艺
Deep Gaussian Processes for geophysical parameter retrieval
论文作者
论文摘要
本文介绍了用于地球物理参数检索的深高斯过程（DGP）。与标准完整的GP模型不同，DGP对复杂（模块化，分层）过程的解释提供了一个有效的解决方案，可以很好地扩展到大型数据集，并提高了与标准完整和稀疏GP模型相比的预测准确性。我们提供了通过红外发声数据估算表面露点温度的经验证据。
This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval. Unlike the standard full GP model, the DGP accounts for complicated (modular, hierarchical) processes, provides an efficient solution that scales well to large datasets, and improves prediction accuracy over standard full and sparse GP models. We give empirical evidence of performance for estimation of surface dew point temperature from infrared sounding data.
