Omre, Henning. creator author. aut http://id.loc.gov/vocabulary/relators/aut Fjeldstad, Torstein M. author. aut http://id.loc.gov/vocabulary/relators/aut Forberg, Ole Bernhard. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access XVII, 226 p. 30 illus. online resource. 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. - 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. by Henning Omre, Torstein M. Fjeldstad, Ole Bernhard Forberg. Statistics  Geographic information systems Bayesian Inference Geographical Information System QA279.5 9783031654183 https://doi.org/10.1007/978-3-031-65418-3 https://doi.org/10.1007/978-3-031-65418-3 241004 20251030113126.0 978-3-031-65418-3