Statistical Modeling Using Bayesian Latent Gaussian Models

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.

Auteur

Dr. Birgir Hrafnkelsson is Professor of Statistics at the University of Iceland. He is an expert in computation for Bayesian latent Gaussian models, spatial statistics, spatio-temporal models, and applications of Bayesian latent Gaussian models in geophysics and environmental sciences. He has worked with experts in glaciology, seismology, engineering seismology, hydrology, meteorology and climatology. His research projects include; fast inference methods for latent Gaussian models; physical-statistical modeling of glacier dynamics; statistical ground motion models for seismic intensity measures; construction of discharge rating curves in open channel flow that make use of the underlying physics; statistical postprocessing for weather forecasting; and statistical models for spatial extremes with applications to temperature, precipitation and flood data.