03775nam a22005175i 4500 978-3-031-65418-3 DE-He213 20251030113126.0 cr nn 008mamaa 241004s2024 sz | s |||| 0|eng d 9783031654183 978-3-031-65418-3 10.1007/978-3-031-65418-3 doi QA279.5 PBTB bicssc MAT029010 bisacsh PBTB thema Omre, Henning. author. aut http://id.loc.gov/vocabulary/relators/aut 85603 Bayesian Spatial Modelling with Conjugate Prior Models [electronic resource] / by Henning Omre, Torstein M. Fjeldstad, Ole Bernhard Forberg. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XVII, 226 p. 30 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda - 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. 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 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. Statistics . 65982 Geographic information systems. 61967 Bayesian Inference. 76436 Geographical Information System. 66422 Fjeldstad, Torstein M. author. aut http://id.loc.gov/vocabulary/relators/aut 85604 Forberg, Ole Bernhard. author. aut http://id.loc.gov/vocabulary/relators/aut 85605 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031654176 Printed edition: 9783031654190 Printed edition: 9783031654206 https://doi.org/10.1007/978-3-031-65418-3 ZDB-2-SMA ZDB-2-SXMS 63992 63992