Preliminary Report on the Sand and Gravel Deposits of Georgia
Author: Livingston Pierson Teas
Publisher:
Published: 1921
Total Pages: 462
ISBN-13:
DOWNLOAD EBOOK →Author: Livingston Pierson Teas
Publisher:
Published: 1921
Total Pages: 462
ISBN-13:
DOWNLOAD EBOOK →Author: Graham Spencer
Publisher: Cambridge University Press
Published: 2015-03-12
Total Pages: 379
ISBN-13: 1107042879
DOWNLOAD EBOOK →The first study of the roles played by senior British officials and civil servants in the Northern Ireland peace process.
Author: Marta Blangiardo
Publisher: John Wiley & Sons
Published: 2015-06-02
Total Pages: 322
ISBN-13: 1118326555
DOWNLOAD EBOOK →Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations
Author: United States. Engineers Corps
Publisher:
Published: 1965
Total Pages: 92
ISBN-13:
DOWNLOAD EBOOK →Author: World Intellectual Property Organization
Publisher: WIPO
Published:
Total Pages: 100
ISBN-13: 928053372X
DOWNLOAD EBOOK →Comprehensive facts, figures and analysis of the international patent system. Special theme: How the COVID-19 crisis affected PCT application filings.
Author: Elias T. Krainski
Publisher: CRC Press
Published: 2018-12-07
Total Pages: 284
ISBN-13: 0429629850
DOWNLOAD EBOOK →Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.
Author: Xiaofeng Wang
Publisher: CRC Press
Published: 2018-01-29
Total Pages: 312
ISBN-13: 1351165755
DOWNLOAD EBOOK →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.