Bayesian maximum entropy analysis and mapping: A farewell to kriging estimators?
Bayesian maximum entropy analysis and mapping: A farewell to kriging estimators?
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DOI：
10.1023/a:1021748324917
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发表时间：
1998-05-01
期刊：
影响因子：
--
通讯作者：
Li, XY
中科院分区：
文献类型：
--
作者：
Christakos, G;Li, XY
贝叶斯最大熵（BME）空间分析和制图方法为在制图过程中纳入先验信息、硬数据和软数据提供了明确的规则。它具有某些独特的特征，使其成为不确定条件下合理推理的忠实守护者。BME是一种通用方法，它不对估计量的线性、潜在概率定律的正态性或空间分布的均匀性做出任何假设。通过利用各种信息和数据来源，BME引入了一个认识论框架，该框架产生的预测地图比传统技术更准确，在许多情况下计算效率更高。事实上，克里金技术可以作为BME方法的特殊情况导出，在关于先验信息和可用数据的限制性假设下。BME是一个更严格的方法比指标克里金纳入软数据。事实上，BME公式适用于空间或时空域，并且其扩展到块和向量随机场的情况是直接的。新的理论结果和数值例子进行了讨论，它使用BME的方法来占重要的知识来源，在一个系统的方式。BME在实际情况中可能是有用的，在实际情况中，先验信息可以用于补偿有限数量的可用测量（例如，初步可行性研究水平）或可与硬数据相结合以显著改进绘图的软数据。BME可以被看作是一种努力，以发展一个更一般的框架，空间/时间分析和绘图，其中包括传统的地质统计学作为其限制情况，它也提供了手段，得出新的结果，也不能得到传统的地质统计学。
The Bayesian Maximum Entropy (BME) method of spatial analysis and mapping provides definite rules for incorporating prior information, hard and soft data into the mapping process. It has certain unique features that make it a loyal guardian of plausible reasoning under conditions of uncertainty. BME is a general approach that does not make any assumptions regarding the linearity of the estimator, the normality of the underlying probability laws, or the homogeneity of the spatial distribution. By capitalizing on various sources of information and data, BME introduces an epistemological framework that produces predictive maps that are more accurate and in many cases computationally more efficient than those derived by traditional techniques. In fact, kriging techniques can be derived as special cases of the BME approach, under restrictive assumptions regarding the prior information and the data available. BME is a more rigorous approach than indicator kriging for incorporating soft data. The BME formulation, in fact, applies in a spatial or a spatiotemporal domain and its extension to the case of block and vector random fields is straightforward. New theoretical results are presented and numerical examples are discussed, which use the BME approach to account for important sources of knowledge in a systematic manner. BME can be useful in practical situations in which prior information can be used to compensate for the limited amount of measurements available (e.g., preliminary of feasibility study levels) or soft data are available that can be combined with hard data to improve mapping significantly. BME may be then viewed as an effort towards the development of a more general framework of spatial/temporal analysis and mapping, which includes traditional geostatistics as its limiting case, and it also provides the means to derive novel results that could nor be obtained by traditional geostatistics.