Bayesian Modeling of Spatio-Temporal Data with R

This book is designed to make spatio-temporal modeling and analysis understandable to students and researchers, mathematicians and statisticians and practitioners in the applied sciences. By avoiding hardcore math and calculus, this book aims to be a bridge that removes the statistical knowledge gap from among the applied scientists.

Sujit K. Sahu is a Professor of Statistics at the University of Southampton. He has co-authored more than 60 papers on Bayesian computation and modeling of spatio-temporal data. He has also contributed to writing specialist R packages for modeling and analysis of such data.

1. Examples of spatio-temporal data 2. Jargon of spatial and spatio-temporal modeling 3. Exploratory data analysis methods 4. Bayesian inference methods 5. Bayesian computation methods 6. Bayesian modeling for point referenced spatial data 7. Bayesian modeling for point referenced spatio-temporal data 8. Practical examples of point referenced data modeling 9. Bayesian forecasting for point referenced data 10. Bayesian modeling for areal unit data 11. Further examples of areal data modeling 12. Gaussian processes for data science and other applications Appendix A. Statistical densities used in the book Appendix B. Answers to selected exercises