Statistical Modeling Using Bayesian Latent Gaussian Models
With Applications in Geophysics and Environmental Sciences
Buch in deiner Nähe kaufen
Beschreibung
This 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.
This 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.
Enhances understanding of statistical modeling and Bayesian inference via real geophysical and environmental examples Gives a thorough overview of Bayesian latent Gaussian models and demonstrates their flexibility Computer code is available for each chapter, enabling readers to get started with code for their own problems
Autor*in
Birgir Hrafnkelsson
Themen in »Statistical Modeling Using Bayesian Latent Gaussian Models«
Bayesian hierarchical models Statistical application in environmental sciences Environmental sciences Statistical modeling Geophysics Physical-statistical modeling Gaussian processes Posterior inference Prior distributions Sea-level rise Spatial modeling of earthquake magnitudes Climate change Weather forecasting