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001484022 019__ $$a1409029061
001484022 020__ $$a9783031397912$$q(electronic bk.)
001484022 020__ $$a3031397916$$q(electronic bk.)
001484022 020__ $$z9783031397905
001484022 020__ $$z3031397908
001484022 0247_ $$a10.1007/978-3-031-39791-2$$2doi
001484022 035__ $$aSP(OCoLC)1409203232
001484022 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCQ$$dOCLCO$$dHTM$$dN$T
001484022 049__ $$aISEA
001484022 050_4 $$aGE45.S73
001484022 08204 $$a363.70072/7$$223/eng/20231219
001484022 24500 $$aStatistical modeling using Bayesian latent Gaussian models :$$bwith applications in geophysics and environmental sciences /$$cBirgir Hrafnkelsson, editor.
001484022 264_1 $$aCham :$$bSpringer,$$c[2023]
001484022 264_4 $$c©2023
001484022 300__ $$a1 online resource (vii, 251 pages) :$$billustrations (some color)
001484022 336__ $$atext$$btxt$$2rdacontent
001484022 337__ $$acomputer$$bc$$2rdamedia
001484022 338__ $$aonline resource$$bcr$$2rdacarrier
001484022 5050_ $$aIntro -- Preface -- Contents -- Bayesian Latent Gaussian Models -- 1 Introduction -- 1.1 Structure of This Chapter and the Book -- 2 The Class of Bayesian Latent Gaussian Models -- 2.1 Bayesian Gaussian-Gaussian Models -- 2.1.1 The Structure of Bayesian Gaussian-Gaussian Models -- 2.1.2 Posterior Inference for Gaussian-Gaussian Models -- 2.1.3 Predictions Based on Gaussian-Gaussian Models -- 2.2 Bayesian LGMs with a Univariate Link Function -- 2.2.1 The Structure of Bayesian LGMs with a Univariate Link Function -- 2.2.2 Posterior Inference for LGMs with a Univariate Link Function Using INLA
001484022 5058_ $$a2.2.3 Predictions Based on LGMs with a Univariate Link Function -- 2.3 Bayesian LGMs with a Multivariate Link Function -- 2.3.1 The Structure of Bayesian LGMs with a Multivariate Link Function -- 2.3.2 Posterior Inference for LGMs with a Multivariate Link Function -- 2.3.3 Predictions Based on LGMs with a Multivariate Link Function -- 3 Priors for the Parameters of Bayesian LGMs -- 3.1 Priors for the Fixed Effects -- 3.2 Priors for the Random Effects -- 3.3 Priors for the Hyperparameters -- 3.3.1 Penalized Complexity Priors
001484022 5058_ $$a3.3.2 PC Priors for Hyperparameters in Common Temporal and Spatial Models -- 3.3.3 Priors for Multiple Variance Parameters -- 4 Application of the Bayesian Gaussian-Gaussian Model-Evaluation of Manning's Formula -- 4.1 The Application and Data -- 4.2 Statistical Model -- 4.3 Inference Scheme -- 4.4 Results -- 5 Application of a Bayesian LGM with a Univariate Link Function-Predicting Chances of Precipitation -- 5.1 The Application and Data -- 5.2 Statistical Model -- 5.3 Inference Scheme -- 5.4 Results -- 6 Application of Bayesian LGMs with a Multivariate Link Function-Three Examples
001484022 5058_ $$a6.1 Seasonal Temperature Forecast -- 6.2 High-dimensional Spatial Extremes -- 6.3 Monthly Precipitation -- Bibliographic Note -- Appendix -- Posterior Computation for the Gaussian-Gaussian Model -- The LGM Split Sampler -- References -- A Review of Bayesian Modelling in Glaciology -- 1 Introduction -- 2 A Synopsis of Bayesian Modelling and Inference in Glaciology -- 2.1 Gaussian-Gaussian Models -- 2.2 Bayesian Hierarchical Models -- 2.3 Bayesian Calibration of Physical Models -- 3 Spatial Prediction of Langjökull Surface Mass Balance -- 4 Assessing Antarctica's Contribution to Sea-Level Rise
001484022 5058_ $$a5 Conclusions and Future Directions -- Appendix: Governing Equations -- References -- Bayesian Discharge Rating Curves Based on the Generalized Power Law -- 1 Introduction -- 2 Data -- 3 Statistical Models -- 4 Posterior Inference -- 5 Results and Software -- 6 Summary -- References -- Bayesian Modeling in Engineering Seismology: Ground-MotionModels -- 1 Introduction -- 2 Ground-Motion Models -- 3 Methods -- 3.1 Regression Analysis -- 3.2 Bayesian Inference -- 4 Applications -- 4.1 Site Effect Characterization Using a Bayesian Hierarchical Model for Array Strong Ground Motions
001484022 506__ $$aAccess limited to authorized users.
001484022 520__ $$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 Antarcticas 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.
001484022 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 21, 2023).
001484022 588__ $$aDescription based on print version record.
001484022 650_6 $$aSciences de l'environnement$$xMéthodes statistiques.
001484022 650_6 $$aProcessus gaussiens.
001484022 650_6 $$aThéorie de la décision bayésienne.
001484022 650_0 $$aEnvironmental sciences$$xStatistical methods.$$xEnvironmental aspects$$0(DLC)sh 89004718
001484022 650_0 $$aGaussian processes.
001484022 650_0 $$aBayesian statistical decision theory.$$0(DLC)sh 85012506
001484022 655_0 $$aElectronic books.
001484022 7001_ $$aHrafnkelsson, Birgir,$$eeditor.
001484022 77608 $$iPrint version:$$tStatistical modeling using Bayesian latent Gaussian models.$$dCham : Springer, [2023]$$z9783031397912$$w(OCoLC)1388319243
001484022 852__ $$bebk
001484022 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-39791-2$$zOnline Access$$91397441.1
001484022 909CO $$ooai:library.usi.edu:1484022$$pGLOBAL_SET
001484022 980__ $$aBIB
001484022 980__ $$aEBOOK
001484022 982__ $$aEbook
001484022 983__ $$aOnline
001484022 994__ $$a92$$bISE