Description 
1 online resource (440 pages) 
Series 
Chapman & Hall/CRC interdisciplinary statistics series 

Interdisciplinary statistics.

Contents 
1. Examples of spatiotemporal data2. Jargon of spatial and spatiotemporal modeling3. Exploratory data analysis methods4. Bayesian inference methods5. Bayesian computation methods6. Bayesian modeling for point referenced spatial data7. Bayesian modeling for point referenced spatiotemporal data8. Practical examples of point referenced data modeling9. Bayesian forecasting for point referenced data10. Bayesian modeling for areal unit data11. Further examples of areal data modeling12. Gaussian processes for data science and other applicationsAppendix A. Statistical densities used in the bookAppendix B. Answers to selected exercises 
Summary 
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 spatiotemporal 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. Key features of the book: ⁰́Ø Accessible detailed discussion of a majority of all aspects of Bayesian methods and computations with worked examples, numerical illustrations and exercises ⁰́Ø A spatial statistics jargon buster chapter that enables the reader to build up a vocabulary without getting clouded in modeling and technicalities ⁰́Ø Computation and modeling illustrations are provided with the help of the dedicated R package bmstdr, allowing the reader to use wellknown packages and platforms, such as rstan, INLA, spBayes, spTimer, spTDyn, CARBayes, CARBayesST, etc ⁰́Ø Included are R code notes detailing the algorithms used to produce all the tables and figures, with data and code available via an online supplement ⁰́Ø Two dedicated chapters discuss practical examples of spatiotemporal modeling of point referenced and areal unit data ⁰́Ø 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 This book is designed to make spatiotemporal 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 spatiotemporal 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 
Bibliography 
Includes bibliographical references (pages 395406) and index 
Notes 
Sujit K. Sahu is a Professor of Statistics at the University of Southampton. He has coauthored more than 60 papers on Bayesian computation and modeling of spatiotemporal data. He has also contributed to writing specialist R packages for modeling and analysis of such data 

Description based on online resource; title from digital title page (viewed on April 13, 2023) 
Subject 
Spatial analysis (Statistics)  Mathematical models


Bayesian statistical decision theory  Data processing


R (Computer program language)


Bayesian statistical decision theory.


MATHEMATICS  Probability & Statistics  Bayesian Analysis.


MATHEMATICS  Probability & Statistics  Regression Analysis.


COMPUTERS  Mathematical & Statistical Software.


Bayesian statistical decision theory

Form 
Electronic book

ISBN 
9780429318443 

0429318448 

9781000543612 

1000543617 

9781000543698 

1000543692 
