| 期刊全稱 | Bayesian Spatial Modelling with Conjugate Prior Models | | 影響因子2023 | Henning Omre,Torstein M. Fjeldstad,Ole Bernhard Fo | | 視頻video | http://file.papertrans.cn/193/192628/192628.mp4 | | 發(fā)行地址 | Defines a unified Bayesian framework for spatial models, covering continuous, event, and mosaic spatial variables.Specifies conjugate pairs of observation likelihood and phenomenon prior spatial model | | 圖書封面 |  | | 影響因子 | .This book offers a comprehensive overview of statistical methodology for modelling and evaluating spatial variables useful in a variety of applications. These spatial variables fall into three categories: continuous, like terrain elevation; events, like tree locations; and mosaics, like medical images...Definitions and discussions of random field models are included for each of these three previously mentioned spatial variable types. Moreover, the readers will have access to algorithms suitable for applying this methodology in practical problem solving, and the computational efficiency of these algorithms are discussed...The presentation is made in a consistent predictive Bayesian framework, which allows separate modelling of the observation acquisition procedure, as a likelihood model, and of the spatial variable characteristics, as a prior spatial model. The likelihood and prior models uniquely define the posterior spatial model, which provides the basis for spatial simulations, spatial predictions with associated precisions, and model parameter inference. The emphasis is on Bayesian spatial modelling with conjugate pairs of likelihood and prior models that are analytically trac | | Pindex | Textbook 2024 |
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