LEADER 04576nam 22006975 450 001 9910760287103321 005 20240621163350.0 010 $a3-031-39791-6 024 7 $a10.1007/978-3-031-39791-2 035 $a(MiAaPQ)EBC30876567 035 $a(Au-PeEL)EBL30876567 035 $a(DE-He213)978-3-031-39791-2 035 $a(CKB)28804793600041 035 $a(EXLCZ)9928804793600041 100 $a20231108d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Modeling Using Bayesian Latent Gaussian Models $eWith Applications in Geophysics and Environmental Sciences /$fedited by Birgir Hrafnkelsson 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (256 pages) 311 08$aPrint version: Hrafnkelsson, Birgir Statistical Modeling Using Bayesian Latent Gaussian Models Cham : Springer International Publishing AG,c2023 9783031397905 327 $aPreface -- Chapter 1. Birgir Hrafnkelsson and Haakon Bakka: Bayesian latent Gaussian models -- Chapter 2. Giri Gopalan, Andrew Zammit-Mangion, and Felicity McCormack: A review of Bayesian modelling in glaciology -- Chapter 3. Birgir Hrafnkelsson, Rafael Daniel Vias, Solvi Rognvaldsson, Axel Orn Jansson, and Sigurdur M. Gardarsson: Bayesian discharge rating curves based on the generalized power law -- Chapter 4. Sahar Rahpeyma, Milad Kowsari, Tim Sonnemann, Benedikt Halldorsson, and Birgir Hrafnkelsson: Bayesian modeling in engineering seismology: Ground-motion models -- Chapter 5. Atefe Darzi, Birgir Hrafnkelsson, and Benedikt Halldorsson: Bayesian modelling in engineering seismology: Spatial earthquake magnitude model -- Chapter 6. Joshua Lovegrove and Stefan Siegert: Improving numerical weather forecasts by Bayesian hierarchical modelling -- Chapter 7. Arnab Hazra, Raphael Huser, and Arni V. Johannesson: Bayesian latent Gaussian models for high-dimensional spatial extremes. 330 $aThis book focuses on the statistical modeling of geophysical and environmental data using Bayesian latent Gaussian models. The structure of these models is described in a thorough introductory chapter, which explains how to construct prior densities for the model parameters, how to infer the parameters using Bayesian computation, and how to use the models to make predictions. The remaining six chapters focus on the application of Bayesian latent Gaussian models to real examples in glaciology, hydrology, engineering seismology, seismology, meteorology and climatology. These examples include: spatial predictions of surface mass balance; the estimation of Antarctica?s contribution to sea-level rise; the estimation of rating curves for the projection of water level to discharge; ground motion models for strong motion; spatial modeling of earthquake magnitudes; weather forecasting based on numerical model forecasts; and extreme value analysis of precipitation on a high-dimensional grid. The book is aimed at graduate students and experts in statistics, geophysics, environmental sciences, engineering, and related fields. 606 $aStatistics 606 $aEarth sciences 606 $aEnvironment 606 $aGeotechnical engineering 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aBayesian Inference 606 $aEarth Sciences 606 $aEnvironmental Sciences 606 $aGeotechnical Engineering and Applied Earth Sciences 606 $aGeofísica$2thub 606 $aEstadística bayesiana$2thub 606 $aMesures gaussianes$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 0$aEarth sciences. 615 0$aEnvironment. 615 0$aGeotechnical engineering. 615 14$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aBayesian Inference. 615 24$aEarth Sciences. 615 24$aEnvironmental Sciences. 615 24$aGeotechnical Engineering and Applied Earth Sciences. 615 7$aGeofísica 615 7$aEstadística bayesiana 615 7$aMesures gaussianes 676 $a363.700727 700 $aHrafnkelsson$b Birgir$01437482 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910760287103321 996 $aStatistical Modeling Using Bayesian Latent Gaussian Models$93598204 997 $aUNINA