Statistical Modeling Using Bayesian Latent Gaussian Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Statistical Modeling Using Bayesian Latent Gaussian Models write by Birgir Hrafnkelsson. This book was released on 2023-12-10. Statistical Modeling Using Bayesian Latent Gaussian Models available in PDF, EPUB and Kindle. 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.
Complex Data Modeling and Computationally Intensive Statistical Methods
Complex Data Modeling and Computationally Intensive Statistical Methods - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Complex Data Modeling and Computationally Intensive Statistical Methods write by Pietro Mantovan. This book was released on 2011-01-27. Complex Data Modeling and Computationally Intensive Statistical Methods available in PDF, EPUB and Kindle. Selected from the conference "S.Co.2009: Complex Data Modeling and Computationally Intensive Methods for Estimation and Prediction," these 20 papers cover the latest in statistical methods and computational techniques for complex and high dimensional datasets.
Gaussian Markov Random Fields
Gaussian Markov Random Fields - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Gaussian Markov Random Fields write by Havard Rue. This book was released on 2005-02-18. Gaussian Markov Random Fields available in PDF, EPUB and Kindle. Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. This book includes extensive case-studie
Bayesian inference with INLA
Bayesian inference with INLA - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian inference with INLA write by Virgilio Gomez-Rubio. This book was released on 2020-02-20. Bayesian inference with INLA available in PDF, EPUB and Kindle. The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time required by typical Markov chain Monte Carlo (MCMC) methods. INLA focuses on marginal inference on the model parameters of latent Gaussian Markov random fields models and exploits conditional independence properties in the model for computational speed. Bayesian Inference with INLA provides a description of INLA and its associated R package for model fitting. This book describes the underlying methodology as well as how to fit a wide range of models with R. Topics covered include generalized linear mixed-effects models, multilevel models, spatial and spatio-temporal models, smoothing methods, survival analysis, imputation of missing values, and mixture models. Advanced features of the INLA package and how to extend the number of priors and latent models available in the package are discussed. All examples in the book are fully reproducible and datasets and R code are available from the book website. This book will be helpful to researchers from different areas with some background in Bayesian inference that want to apply the INLA method in their work. The examples cover topics on biostatistics, econometrics, education, environmental science, epidemiology, public health, and the social sciences.
Bayesian Regression Modeling with INLA
Bayesian Regression Modeling with INLA - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Bayesian Regression Modeling with INLA write by Xiaofeng Wang. This book was released on 2018-01-29. Bayesian Regression Modeling with INLA available in PDF, EPUB and Kindle. INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (MCMC), the standard tool for Bayesian inference. Bayesian Regression Modeling with INLA covers a wide range of modern regression models and focuses on the INLA technique for building Bayesian models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to demonstrate the interplay of theory and practice with reproducible studies. Complete R commands are provided for each example, and a supporting website holds all of the data described in the book. An R package including the data and additional functions in the book is available to download. The book is aimed at readers who have a basic knowledge of statistical theory and Bayesian methodology. It gets readers up to date on the latest in Bayesian inference using INLA and prepares them for sophisticated, real-world work. Xiaofeng Wang is Professor of Medicine and Biostatistics at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and a Full Staff in the Department of Quantitative Health Sciences at Cleveland Clinic. Yu Ryan Yue is Associate Professor of Statistics in the Paul H. Chook Department of Information Systems and Statistics at Baruch College, The City University of New York. Julian J. Faraway is Professor of Statistics in the Department of Mathematical Sciences at the University of Bath.
