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Record 5 of 44
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You searched IISERK - Special Collections: Government Publications
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Call Number
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519.542 SAR2
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Author
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Sahu, Sujit Kumar, author.
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Title
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Bayesian modeling of spatio-temporal data with R / Sujit K. Sahu.
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Material Info.
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xxii, 411 pages: illustrations; 24 cm.
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Series
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Chapman & Hall/CRC Interdisciplinary Statistics
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Series
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Chapman & Hall/CRC Interdisciplinary Statistics
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Summary Note
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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. 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 well-known
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 spatio-temporal 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 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.
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Notes
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Includes bibliographic references and index.
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Notes
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1. Examples of spatio-temporal data2. Jargon of spatial
and spatio-temporal modeling3. Exploratory data analysis
methods4. Bayesian inference methods5. Bayesian
computation methods6. Bayesian modeling for point
referenced spatial data7. Bayesian modeling for point
referenced spatio-temporal 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
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ISBN
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9780367277987
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ISBN
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9781032209579
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ISBN
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0367277980
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ISBN
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1032209577
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Subject
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Bayesian statistical decision theory.
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Date
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Year, Month, Day:02211011
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| Purchase Order Status |
| PO ID |
Line Number |
Destination |
Order Quantity |
Received Quantity |
Latest Order Modification Date |
Last Received Date |
| 354 |
4 |
The Librarian |
1 |
1 |
5-12-2022 16:07 |
11-1-2022 09:40 |
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