Modeling and Estimation of Heterogeneous Spatiotemporal Attributes Under Conditions of Uncertainty
Modeling and Estimation of Heterogeneous Spatiotemporal Attributes Under Conditions of Uncertainty
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DOI：
10.1109/tgrs.2010.2052624
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发表时间：
2011
影响因子：
8.2
通讯作者：
Hwa-Lung Yu;G. Christakos
中科院分区：
文献类型：
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作者：
Hwa-Lung Yu;G. Christakos
提出了一种研究不确定条件下具有非均匀时空变化的属性的随机方法。该方法是广义时空随机场理论和贝叶斯最大熵推理模式的综合。这种概念性综合的结果是一种通用的时空数据处理和属性估计(预测)方法，它表现出许多吸引人的特点，包括：该方法对属性估计器的线性和正态分布没有限制性假设(自动合并非线性估计器和非高斯概率律)，它可以研究具有异质时空依赖模式的属性，并且它可以解释各种知识(核心和特定于属性)。该方法是通用的，可用于研究与各种系统(物理、技术、医疗和社会)相关联的属性。通过数值实验和真实世界的案例研究，深入了解了该方法的计算实现和比较性能。
A stochastic method is presented for studying attributes with heterogeneous space-time variations under conditions of uncertainty. The method is a synthesis of the generalized spatiotemporal random field theory and the Bayesian maximum entropy mode of reasoning. The result of this conceptual synthesis is a general and versatile method of spatiotemporal data processing and attribute estimation (prediction) that exhibits a number of attractive features, including the following: The method makes no restrictive assumptions concerning the linearity and normality of the attribute estimator (nonlinear estimators and non-Gaussian probability laws are automatically incorporated), it can study attributes with heterogeneous space-time dependence patterns, and it can account for various kinds of knowledge (core and attribute specific). The method is general, and it can be used to study attributes associated with a variety of systems (physical, technical, medical, and social). Insight into the computational implementation and comparative performance of the proposed method is gained by means of numerical experiments and a real-world case study.