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Bayesian Spatial Modeling of Precipitation Data
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 Title & Authors
Bayesian Spatial Modeling of Precipitation Data
Heo, Tae-Young; Park, Man-Sik;
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 Abstract
Spatial models suitable for describing the evolving random fields in climate and environmental systems have been developed by many researchers. In general, rainfall in South Korea is highly variable in intensity and amount across space. This study characterizes the monthly and regional variation of rainfall fields using the spatial modeling. The main objective of this research is spatial prediction with the Bayesian hierarchical modeling (kriging) in order to further our understanding of water resources over space. We use the Bayesian approach in order to estimate the parameters and produce more reliable prediction. The Bayesian kriging also provides a promising solution for analyzing and predicting rainfall data.
 Keywords
Precipitation;Bayesian kriging;Markov Chain Monte Carlo;
 Language
English
 Cited by
1.
Spatial Prediction Based on the Bayesian Kriging with Box-Cox Transformation,;;

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2.
풍속 자료의 공간예측,정승환;박만식;김기환;

응용통계연구, 2010. vol.23. 2, pp.345-356 crossref(new window)
3.
다양한 관측네트워크에서 얻은 공간자료들을 활용한 계층모형 구축,최지은;박만식;

응용통계연구, 2013. vol.26. 1, pp.93-109 crossref(new window)
1.
On the Hierarchical Modeling of Spatial Measurements from Different Station Networks, Korean Journal of Applied Statistics, 2013, 26, 1, 93  crossref(new windwow)
2.
Spatial Prediction of Wind Speed Data, Korean Journal of Applied Statistics, 2010, 23, 2, 345  crossref(new windwow)
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