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Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software
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Using R for Bayesian Spatial and Spatio-Temporal Health Modeling (Hardcover)
Loot Price: R4,072
Discovery Miles 40 720
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Using R for Bayesian Spatial and Spatio-Temporal Health Modeling (Hardcover)
Series: Chapman & Hall/CRC The R Series
Expected to ship within 10 - 15 working days
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Progressively more and more attention has been paid to how location
affects health outcomes. The area of disease mapping focusses on
these problems, and the Bayesian paradigm has a major role to play
in the understanding of the complex interplay of context and
individual predisposition in such studies of disease. Using R for
Bayesian Spatial and Spatio-Temporal Health Modeling provides a
major resource for those interested in applying Bayesian
methodology in small area health data studies. Features: Review of
R graphics relevant to spatial health data Overview of Bayesian
methods and Bayesian hierarchical modeling as applied to spatial
data Bayesian Computation and goodness-of-fit Review of basic
Bayesian disease mapping models Spatio-temporal modeling with MCMC
and INLA Special topics include multivariate models, survival
analysis, missing data, measurement error, variable selection,
individual event modeling, and infectious disease modeling Software
for fitting models based on BRugs, Nimble, CARBayes and INLA
Provides code relevant to fitting all examples throughout the book
at a supplementary website The book fills a void in the literature
and available software, providing a crucial link for students and
professionals alike to engage in the analysis of spatial and
spatio-temporal health data from a Bayesian perspective using R.
The book emphasizes the use of MCMC via Nimble, BRugs, and
CARBAyes, but also includes INLA for comparative purposes. In
addition, a wide range of packages useful in the analysis of
geo-referenced spatial data are employed and code is provided. It
will likely become a key reference for researchers and students
from biostatistics, epidemiology, public health, and environmental
science.
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