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020 _a9783031654183
_9978-3-031-65418-3
024 7 _a10.1007/978-3-031-65418-3
_2doi
050 4 _aQA279.5
072 7 _aPBTB
_2bicssc
072 7 _aMAT029010
_2bisacsh
072 7 _aPBTB
_2thema
100 1 _aOmre, Henning.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985603
245 1 0 _aBayesian Spatial Modelling with Conjugate Prior Models
_h[electronic resource] /
_cby Henning Omre, Torstein M. Fjeldstad, Ole Bernhard Forberg.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVII, 226 p. 30 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.
650 0 _aStatistics .
_965982
650 0 _aGeographic information systems.
_961967
650 1 4 _aBayesian Inference.
_976436
650 2 4 _aGeographical Information System.
_966422
700 1 _aFjeldstad, Torstein M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985604
700 1 _aForberg, Ole Bernhard.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985605
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031654176
776 0 8 _iPrinted edition:
_z9783031654190
776 0 8 _iPrinted edition:
_z9783031654206
856 4 0 _uhttps://doi.org/10.1007/978-3-031-65418-3
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c63992
_d63992