Bayesian modeling of spatio-temporal data with R/ Sujit K Saha

Includes bibliography and index

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 forcasting for point referenced data -- Bayesian modeling for areal unit data -- Further examples of areal data modeling -- Gaussan processes for data science and other applications -- Appendix A: Statistical densities used in the book -- Appendix B: Answers to selected exercises

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.