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sahu s bayesian modeling spatio temporal data r 2022

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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

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Sahu S. Bayesian Modeling of Spatio-Temporal Data with R 2022.pdf 60.7 MB

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sahu s bayesian modeling spatio temporal data r 2022

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