03408nam a22004335i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001200153072001700165072002300182072001600205100007600221245014700297250001800444264006900462300004500531336002600576337002600602338003600628347002400664505025800688520153100946650001802477650003602495650002402531650003702555700008402592700008402676710003402760773002602794776003602820776003602856776003602892856004602928978-3-031-65418-3DE-He21320251030113126.0cr nn 008mamaa241004s2024    sz |    s    |||| 0|eng d  a97830316541837 a10.1007/978-3-031-65418-32doi 4aQA279.5 7aPBTB2bicssc 7aMAT0290102bisacsh 7aPBTB2thema1 aOmre, Henning.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aBayesian Spatial Modelling with Conjugate Prior Modelsh[electronic resource] /cby Henning Omre, Torstein M. Fjeldstad, Ole Bernhard Forberg.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXVII, 226 p. 30 illus.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 a- Introduction -- Bayesian Spatial Modelling -- Conjugate Inversion Models -- Random Fields -- Part I Traditional Conjugate Spatial Models -- Likelihood Models -- Prior Models -- Posterior Models -- Model Parameter Inference -- Computational Challenges.  aThis 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 tractable and hence suitable for data abundant spatial studies. Alternative methods frequently used in spatial statistics are presented using a unified notation. The book is suitable as a textbook for a ‘Spatial Statistics’ course at the MSc or PhD level, as it also includes algorithm descriptions, project texts, and exercises. 0aStatistics . 0aGeographic information systems.14aBayesian Inference.24aGeographical Information System.1 aFjeldstad, Torstein M.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aForberg, Ole Bernhard.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303165417608iPrinted edition:z978303165419008iPrinted edition:z978303165420640uhttps://doi.org/10.1007/978-3-031-65418-3