Bayesian Modeling of Spatio-Temporal Data with R

  1. Sahu, S. K. (2021a) Bayesian modeling of spatio-temporal data with R. Chapman and Hall (in press). Available from https://www.sujitsahu.com/bmbook/bmstdrbook.pdf
    Download size 40MB.
  2. Sahu, S. K. (2021b) bmstdr: Bayesian Modeling of Spatio-Temporal Data with R. Available from https://github.com/sujit-sahu/bmstdr/

    This is the companion R package. Please click here for download and installation information.

Book Description

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 modeling as modeling automatically aids in estimation of uncertainties in all conclusions drawn from such data. Unified Bayesian modeling, implemented through user friendly software packages, provides a crucial key to unlocking the full power of these methods for solving challenging practical problems.

Keeping the applied scientists in mind, this book presents most of the modeling with the help of R commands written in a purposefully developed R package to facilitate spatio-temporal modeling. However, the presentation in the book does not lose sight of mathematical and statistical rigor as it presents the underlying theories of Bayesian inference and computation in stand alone chapters in the first part which would be appealing to mathematics/statistics major final year undergraduate or post-graduate students who are in search of such modeling.

Key features of the book:

  • Accessible detailed discussion of a majority of all aspects of Bayesian methods and computations with worked examples, numerical illustrations and exercises with which the reader should be able to experience the methodologies live.
  • A spatial statistics jargon buster chapter that enables the reader to build up a vocabulary without getting clouded in modeling and technicalities in model fitting.
  • Computation and modeling illustrations are provided with the help of the dedicated R package bmstdr. The look and feel of the model fitting commands and their output resemble that of the lm command in R. A novice user, who is otherwise familiar with the lm command, will quickly be able to perform spatio-temporal modeling using well-known packages and platforms such rstan, INLA, spBayes, spTimer, spTDyn, CARBayes and CARBayesST.
  • Included are R code notes detailing the algorithms used to produce all the tables and figures. An online supplement presents the necessary data and the full code for reproducing these results.
  • Two dedicated chapters discuss practical examples of spatio-temporal modeling of point referenced and areal unit data. Taken from a variety of disciplines all illustrations are practical data driven rather than simulation based.
  • Throughout, the emphasis has been on validating models by splitting data into test and training sets following on the philosophy of machine learning and data science. The last chapter consolidates this connection formally by bringing in the Gaussian process based machine learning into the context of the topics presented in the book.

This book is designed to make spatio-temporal modeling and analysis accessible and understandable to a wide audience from bachelors, masters and PhD students to researchers, from mathematicians and statisticians to practitioners in applied sciences. 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.