A BAYESIAN MAXIMUM-ENTROPY VIEW TO THE SPATIAL ESTIMATION PROBLEM
A BAYESIAN MAXIMUM-ENTROPY VIEW TO THE SPATIAL ESTIMATION PROBLEM
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DOI：
10.1007/bf00890661
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发表时间：
1990-10-01
期刊：
影响因子：
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通讯作者：
CHRISTAKOS, G
中科院分区：
文献类型：
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作者：
CHRISTAKOS, G
本文的目的是强调贝叶斯/最大熵视图对空间估计问题的重要性。根据这一观点，估计方程是通过平衡两个要求的过程出现的：关于空间变异性的高先验信息和关于估计地图的高后验概率。第一个要求使用各种先验信息源，并涉及熵函数的最大化。第二个要求导致所谓的贝叶斯函数的最大化。某些基本的结果和有吸引力的功能，所提出的方法的背景下，随机场理论进行了讨论，并提出了一个系统的空间估计方案。后者满足各种有用的属性超出了传统的随机估计方法所隐含的。
The purpose of this paper is to stress the importance of a Bayesian/maximum-entropy view toward the spatial estimation problem. According to this view, the estimation equations emerge through a process that balances two requirements: High prior information about the spatial variability and high posterior probability about the estimated map. The first requirement uses a variety of sources of prior information and involves the maximization of an entropy function. The second requirement leads to the maximization of a so-called Bayes function. Certain fundamental results and attractive features of the proposed approach in the context of the random field theory are discussed, and a systematic spatial estimation scheme is presented. The latter satisfies a variety of useful properties beyond those implied by the traditional stochastic estimation methods.