Sahu S. Bayesian Modeling Of Spatio-Temporal Data With R 2022

Textbook in PDF format

Applied sciences, both physical and social, such as atmospheric, biological, climate, demographic, economic, ecological, environmental, oceanic and political, routinely gather large volumes of spatial and spatio-temporal data in order to make wide ranging inference and prediction. Ideally such inferential tasks should be approached through modelling, which aids in estimation of uncertainties in all conclusions drawn from such data. Unified Bayesian modelling, implemented through user friendly software packages, provides a crucial key to unlocking the full power of these methods for solving challenging practical problems. This book is designed to make spatio-temporal modeling and analysis accessible and understandable to a wide audience of students and researchers, from mathematicians and statisticians to practitioners in the applied sciences. It presents most of the modeling with the help of R commands written in a purposefully developed R package to facilitate spatio-temporal modeling. It does not compromise on rigour, as it presents the underlying theories of Bayesian inference and computation in standalone chapters, which would be appeal those interested in the theoretical details. By avoiding hard core mathematics and calculus, this book aims to be a bridge that removes the statistical knowledge gap from among the applied scientists.
Examples of spatio-temporal data
Jargon of spatial and spatio-temporal modeling
Exploratory data analysis methods
Bayesian inference methods
Bayesian computation methods
Bayesian modeling for point referenced spatial data
Bayesian modeling for point referenced spatio-temporal data
Practical examples of point referenced data modeling
Bayesian forecasting for point referenced data
Bayesian modeling for areal unit data
Further examples of areal data modeling
Gaussian processes for data science and other applications