The circular Matérn covariance function and its link to Markov random fields on the circle

IF 2.1 2区 数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY
Chunfeng Huang , Ao Li , Nicholas W. Bussberg , Haimeng Zhang
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引用次数: 0

Abstract

The connection between Gaussian random fields and Markov random fields has been well-established in Euclidean spaces, with Matérn covariance functions playing a pivotal role. In this paper, we explore the extension of this link to circular spaces and uncover different results. It is known that Matérn covariance functions are not always positive definite on the circle; however, the circular Matérn covariance functions are shown to be valid on the circle and are the focus of this paper. For these circular Matérn random fields on the circle, we show that the corresponding Markov random fields can be obtained explicitly on equidistance grids. Consequently, the equivalence between the circular Matérn random fields and Markov random fields is then exact and this marks a departure from the Euclidean space counterpart, where only approximations are achieved. Moreover, the key motivation in Euclidean spaces for establishing such link relies on the assumption that the corresponding Markov random field is sparse. We show that such sparsity does not hold in general on the circle. In addition, for the sparse Markov random field on the circle, we derive its corresponding Gaussian random field.

圆马特恩协方差函数及其与圆上马尔可夫随机场的联系
高斯随机场和马尔可夫随机场之间的联系在欧几里得空间中已经得到了很好的证实,其中马特恩协方差函数发挥了关键作用。在本文中,我们将探索这一联系在圆空间中的延伸,并揭示出不同的结果。众所周知,圆上的 Matérn 协方差函数并不总是正定的;然而,圆上的 Matérn 协方差函数被证明在圆上是有效的,这也是本文的重点。对于圆上的这些圆 Matérn 随机场,我们证明相应的马尔可夫随机场可以在等距网格上明确得到。因此,圆 Matérn 随机场和马尔可夫随机场之间的等价性是精确的,这标志着与欧几里得空间对应场的不同,后者只能得到近似值。此外,欧几里得空间中建立这种联系的关键动机依赖于假设相应的马尔可夫随机场是稀疏的。我们证明,这种稀疏性在圆上一般不成立。此外,对于圆上的稀疏马尔科夫随机场,我们推导出了其相应的高斯随机场。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Spatial Statistics
Spatial Statistics GEOSCIENCES, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.00
自引率
21.70%
发文量
89
审稿时长
55 days
期刊介绍: Spatial Statistics publishes articles on the theory and application of spatial and spatio-temporal statistics. It favours manuscripts that present theory generated by new applications, or in which new theory is applied to an important practical case. A purely theoretical study will only rarely be accepted. Pure case studies without methodological development are not acceptable for publication. Spatial statistics concerns the quantitative analysis of spatial and spatio-temporal data, including their statistical dependencies, accuracy and uncertainties. Methodology for spatial statistics is typically found in probability theory, stochastic modelling and mathematical statistics as well as in information science. Spatial statistics is used in mapping, assessing spatial data quality, sampling design optimisation, modelling of dependence structures, and drawing of valid inference from a limited set of spatio-temporal data.
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