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This book is about building platforms for pandemic prediction. It
provides an overview of probabilistic prediction for pandemic
modeling based on a data-driven approach. It also provides guidance
on building platforms with currently available technology using
tools such as R, Shiny, and interactive plotting programs. The
focus is on the integration of statistics and computing tools
rather than on an in-depth analysis of all possibilities on each
side. Readers can follow different reading paths through the book,
depending on their needs. The book is meant as a basis for further
investigation of statistical modelling, implementation tools,
monitoring aspects, and software functionalities. Features: A
general but parsimonious class of models to perform statistical
prediction for epidemics, using a Bayesian approach Implementation
of automated routines to obtain daily prediction results How to
interactively visualize the model results Strategies for monitoring
the performance of the predictions and identifying potential issues
in the results Discusses the many decisions required to develop and
publish online platforms Supplemented by an R package and its
specific functionalities to model epidemic outbreaks The book is
geared towards practitioners with an interest in the development
and presentation of results in an online platform of statistical
analysis of epidemiological data. The primary audience includes
applied statisticians, biostatisticians, computer scientists,
epidemiologists, and professionals interested in learning more
about epidemic modelling in general, including the COVID-19
pandemic, and platform building. The authors are professors at the
Statistics Department at Universidade Federal de Minas Gerais.
Their research records exhibit contributions applied to a number of
areas of Science, including Epidemiology. Their research activities
include books published with Chapman and Hall/CRC and papers in
high quality journals. They have also been involved with academic
management of graduate programs in Statistics and one of them is
currently the President of the Brazilian Statistical Association.
This book is about building platforms for pandemic prediction. It
provides an overview of probabilistic prediction for pandemic
modeling based on a data-driven approach. It also provides guidance
on building platforms with currently available technology using
tools such as R, Shiny, and interactive plotting programs. The
focus is on the integration of statistics and computing tools
rather than on an in-depth analysis of all possibilities on each
side. Readers can follow different reading paths through the book,
depending on their needs. The book is meant as a basis for further
investigation of statistical modelling, implementation tools,
monitoring aspects, and software functionalities. Features: A
general but parsimonious class of models to perform statistical
prediction for epidemics, using a Bayesian approach Implementation
of automated routines to obtain daily prediction results How to
interactively visualize the model results Strategies for monitoring
the performance of the predictions and identifying potential issues
in the results Discusses the many decisions required to develop and
publish online platforms Supplemented by an R package and its
specific functionalities to model epidemic outbreaks The book is
geared towards practitioners with an interest in the development
and presentation of results in an online platform of statistical
analysis of epidemiological data. The primary audience includes
applied statisticians, biostatisticians, computer scientists,
epidemiologists, and professionals interested in learning more
about epidemic modelling in general, including the COVID-19
pandemic, and platform building. The authors are professors at the
Statistics Department at Universidade Federal de Minas Gerais.
Their research records exhibit contributions applied to a number of
areas of Science, including Epidemiology. Their research activities
include books published with Chapman and Hall/CRC and papers in
high quality journals. They have also been involved with academic
management of graduate programs in Statistics and one of them is
currently the President of the Brazilian Statistical Association.
A Balanced Treatment of Bayesian and Frequentist Inference
Statistical Inference: An Integrated Approach, Second Edition
presents an account of the Bayesian and frequentist approaches to
statistical inference. Now with an additional author, this second
edition places a more balanced emphasis on both perspectives than
the first edition. New to the Second Edition New material on
empirical Bayes and penalized likelihoods and their impact on
regression models Expanded material on hypothesis testing, method
of moments, bias correction, and hierarchical models More examples
and exercises More comparison between the approaches, including
their similarities and differences Designed for advanced
undergraduate and graduate courses, the text thoroughly covers
statistical inference without delving too deep into technical
details. It compares the Bayesian and frequentist schools of
thought and explores procedures that lie on the border between the
two. Many examples illustrate the methods and models, and exercises
are included at the end of each chapter.
While there have been few theoretical contributions on the Markov
Chain Monte Carlo (MCMC) methods in the past decade, current
understanding and application of MCMC to the solution of inference
problems has increased by leaps and bounds. Incorporating changes
in theory and highlighting new applications, Markov Chain Monte
Carlo: Stochastic Simulation for Bayesian Inference, Second Edition
presents a concise, accessible, and comprehensive introduction to
the methods of this valuable simulation technique. The second
edition includes access to an internet site that provides the code,
written in R and WinBUGS, used in many of the previously existing
and new examples and exercises. More importantly, the
self-explanatory nature of the codes will enable modification of
the inputs to the codes and variation on many directions will be
available for further exploration. Major changes from the previous
edition: * More examples with discussion of computational details
in chapters on Gibbs sampling and Metropolis-Hastings algorithms *
Recent developments in MCMC, including reversible jump, slice
sampling, bridge sampling, path sampling, multiple-try, and delayed
rejection * Discussion of computation using both R and WinBUGS *
Additional exercises and selected solutions within the text, with
all data sets and software available for download from the Web *
Sections on spatial models and model adequacy The self-contained
text units make MCMC accessible to scientists in other disciplines
as well as statisticians. The book will appeal to everyone working
with MCMC techniques, especially research and graduate
statisticians and biostatisticians, and scientists handling data
and formulating models. The book has been substantially reinforced
as a first reading of material on MCMC and, consequently, as a
textbook for modern Bayesian computation and Bayesian inference
courses.
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