Statistical Modeling Using Bayesian Latent Gaussian Models: With Applications in Geophysics and Environmental Sciences

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.