Sujit Sahu“时空数据的贝叶斯建模与R”综述

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics Pub Date : 2023-10-31 DOI:10.1016/j.spasta.2023.100788

Patrick E. Brown

引用次数: 0

摘要

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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Review of Sujit Sahu’s “Bayesian modeling of spatio-temporal data with R”

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来源期刊

Spatial Statistics GEOSCIENCES, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

4.00

自引率

21.70%

发文量

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.