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: * Accessible detailed discussion of a
majority of all aspects of Bayesian methods and computations with
worked examples, numerical illustrations and exercises * A spatial
statistics jargon buster chapter that enables the reader to build
up a vocabulary without getting clouded in modeling and
technicalities * 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 *
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 * Two dedicated chapters discuss practical
examples of spatio-temporal modeling of point referenced and areal
unit data * 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.
General
Imprint: |
Crc Press
|
Country of origin: |
United Kingdom |
Series: |
Chapman & Hall/CRC Interdisciplinary Statistics |
Release date: |
February 2022 |
First published: |
2022 |
Authors: |
Sujit Sahu
|
Dimensions: |
234 x 156 x 28mm (L x W x T) |
Format: |
Hardcover
|
Pages: |
411 |
ISBN-13: |
978-0-367-27798-7 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
LSN: |
0-367-27798-0 |
Barcode: |
9780367277987 |
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