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Bayesian spatial modelling with conjugate prior models

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian spatial modelling with conjugate prior models/ by Henning Omre, Torstein M. Fjeldstad, Ole Bernhard Forberg.
Author:	Omre, Henning.
other author:	Fjeldstad, Torstein M.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xvii, 226 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Spatial analysis (Statistics) - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-031-65418-3
ISBN:	9783031654183

Bayesian spatial modelling with conjugate prior models
Omre, Henning.

Bayesian spatial modelling with conjugate prior models[electronic resource] /by Henning Omre, Torstein M. Fjeldstad, Ole Bernhard Forberg. - Cham :Springer Nature Switzerland :2024. - xvii, 226 p. :ill., digital ;24 cm.

- 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.

ISBN: 9783031654183

Standard No.: 10.1007/978-3-031-65418-3doiSubjects--Topical Terms:

965588
Spatial analysis (Statistics)
--Mathematical models.

LC Class. No.: QA278.2

Dewey Class. No.: 001.4226

Bayesian spatial modelling with conjugate prior models
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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 $a 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.
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