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Bayesian Analysis of Infectious Diseases -COVID-19 and Beyond shows
how the Bayesian approach can be used to analyze the evolutionary
behavior of infectious diseases, including the coronavirus
pandemic. The book describes the foundation of Bayesian statistics
while explicating the biology and evolutionary behavior of
infectious diseases, including viral and bacterial manifestations
of the contagion. The book discusses the application of Markov
Chains to contagious diseases, previews data analysis models, the
epidemic threshold theorem, and basic properties of the infection
process. Also described are the chain binomial model for the
evolution of epidemics. Features: Represents the first book on
infectious disease from a Bayesian perspective. Employs WinBUGS and
R to generate observations that follow the course of contagious
maladies. Includes discussion of the coronavirus pandemic as well
as many examples from the past, including the flu epidemic of
1918-1919. Compares standard non-Bayesian and Bayesian inferences.
Offers the R and WinBUGS code on at www.routledge.com/9780367633868
In many branches of science relevant observations are taken
sequentially over time. Bayesian Analysis of Time Series discusses
how to use models that explain the probabilistic characteristics of
these time series and then utilizes the Bayesian approach to make
inferences about their parameters. This is done by taking the prior
information and via Bayes theorem implementing Bayesian inferences
of estimation, testing hypotheses, and prediction. The methods are
demonstrated using both R and WinBUGS. The R package is primarily
used to generate observations from a given time series model, while
the WinBUGS packages allows one to perform a posterior analysis
that provides a way to determine the characteristic of the
posterior distribution of the unknown parameters. Features Presents
a comprehensive introduction to the Bayesian analysis of time
series. Gives many examples over a wide variety of fields including
biology, agriculture, business, economics, sociology, and
astronomy. Contains numerous exercises at the end of each chapter
many of which use R and WinBUGS. Can be used in graduate courses in
statistics and biostatistics, but is also appropriate for
researchers, practitioners and consulting statisticians. About the
author Lyle D. Broemeling, Ph.D., is Director of Broemeling and
Associates Inc., and is a consulting biostatistician. He has been
involved with academic health science centers for about 20 years
and has taught and been a consultant at the University of Texas
Medical Branch in Galveston, The University of Texas MD Anderson
Cancer Center and the University of Texas School of Public Health.
His main interest is in developing Bayesian methods for use in
medical and biological problems and in authoring textbooks in
statistics. His previous books for Chapman & Hall/CRC include
Bayesian Biostatistics and Diagnostic Medicine, and Bayesian
Methods for Agreement.
Bayesian Analysis of Infectious Diseases -COVID-19 and Beyond shows
how the Bayesian approach can be used to analyze the evolutionary
behavior of infectious diseases, including the coronavirus
pandemic. The book describes the foundation of Bayesian statistics
while explicating the biology and evolutionary behavior of
infectious diseases, including viral and bacterial manifestations
of the contagion. The book discusses the application of Markov
Chains to contagious diseases, previews data analysis models, the
epidemic threshold theorem, and basic properties of the infection
process. Also described are the chain binomial model for the
evolution of epidemics. Features: Represents the first book on
infectious disease from a Bayesian perspective. Employs WinBUGS and
R to generate observations that follow the course of contagious
maladies. Includes discussion of the coronavirus pandemic as well
as many examples from the past, including the flu epidemic of
1918-1919. Compares standard non-Bayesian and Bayesian inferences.
Offers the R and WinBUGS code on at www.routledge.com/9780367633868
There are numerous advantages to using Bayesian methods in
diagnostic medicine, which is why they are employed more and more
today in clinical studies. Exploring Bayesian statistics at an
introductory level, Bayesian Biostatistics and Diagnostic Medicine
illustrates how to apply these methods to solve important problems
in medicine and biology. After focusing on the wide range of areas
where diagnostic medicine is used, the book introduces Bayesian
statistics and the estimation of accuracy by sensitivity,
specificity, and positive and negative predictive values for
ordinal and continuous diagnostic measurements. The author then
discusses patient covariate information and the statistical methods
for estimating the agreement among observers. The book also
explains the protocol review process for cancer clinical trials,
how tumor responses are categorized, how to use WHO and RECIST
criteria, and how Bayesian sequential methods are employed to
monitor trials and estimate sample sizes. With many tables and
figures, this book enables readers to conduct a Bayesian analysis
for a large variety of interesting and practical biomedical
problems.
Useful in many areas of medicine and biology, Bayesian methods are
particularly attractive tools for the design of clinical trials and
diagnostic tests, which are based on established information,
usually from related previous studies. Advanced Bayesian Methods
for Medical Test Accuracy begins with a review of the usual
measures such as specificity, sensitivity, positive and negative
predictive value, and the area under the ROC curve. Then the scope
expands to cover the more advanced topics of verification bias,
diagnostic tests with imperfect gold standards, and those for which
no gold standard is available. Promoting accuracy and efficiency of
clinical trials, tests, and the diagnostic process, this book:
Enables the user to efficiently apply prior information via a
WinBUGS package Presents many ideas for the first time and goes far
beyond the two standard references Integrates reader agreement with
different modalities-X-ray, CT Scanners, and more-to study their
effect on medical test accuracy Provides practical chapter-end
problems Useful for graduate students and consulting statisticians
working in the various areas of diagnostic medicine and study
design, this practical resource introduces the fundamentals of
programming and executing BUGS, giving readers the tools and
experience to successfully analyze studies for medical test
accuracy.
With Bayesian statistics rapidly becoming accepted as a way to
solve applied statisticalproblems, the need for a comprehensive,
up-to-date source on the latest advances in thisfield has
arisen.Presenting the basic theory of a large variety of linear
models from a Bayesian viewpoint,Bayesian Analysis of Linear Models
fills this need. Plus, this definitive volume containssomething
traditional-a review of Bayesian techniques and methods of
estimation, hypothesis,testing, and forecasting as applied to the
standard populations ... somethinginnovative-a new approach to
mixed models and models not generally studied by statisticianssuch
as linear dynamic systems and changing parameter models ... and
somethingpractical-clear graphs, eary-to-understand examples,
end-of-chapter problems, numerousreferences, and a distribution
appendix.Comprehensible, unique, and in-depth, Bayesian Analysis of
Linear Models is the definitivemonograph for statisticians,
econometricians, and engineers. In addition, this text isideal for
students in graduate-level courses such as linear models,
econometrics, andBayesian inference.
This is the first book designed to introduce Bayesian inference
procedures for stochastic processes. There are clear advantages to
the Bayesian approach (including the optimal use of prior
information). Initially, the book begins with a brief review of
Bayesian inference and uses many examples relevant to the analysis
of stochastic processes, including the four major types, namely
those with discrete time and discrete state space and continuous
time and continuous state space. The elements necessary to
understanding stochastic processes are then introduced, followed by
chapters devoted to the Bayesian analysis of such processes. It is
important that a chapter devoted to the fundamental concepts in
stochastic processes is included. Bayesian inference (estimation,
testing hypotheses, and prediction) for discrete time Markov
chains, for Markov jump processes, for normal processes (e.g.
Brownian motion and the Ornstein-Uhlenbeck process), for
traditional time series, and, lastly, for point and spatial
processes are described in detail. Heavy emphasis is placed on many
examples taken from biology and other scientific disciplines. In
order analyses of stochastic processes, it will use R and WinBUGS.
Features: Uses the Bayesian approach to make statistical Inferences
about stochastic processes The R package is used to simulate
realizations from different types of processes Based on
realizations from stochastic processes, the WinBUGS package will
provide the Bayesian analysis (estimation, testing hypotheses, and
prediction) for the unknown parameters of stochastic processes To
illustrate the Bayesian inference, many examples taken from
biology, economics, and astronomy will reinforce the basic concepts
of the subject A practical approach is implemented by considering
realistic examples of interest to the scientific community WinBUGS
and R code are provided in the text, allowing the reader to easily
verify the results of the inferential procedures found in the many
examples of the book Readers with a good background in two areas,
probability theory and statistical inference, should be able to
master the essential ideas of this book.
Written by a biostatistics expert with over 20 years of experience
in the field, Bayesian Methods in Epidemiology presents statistical
methods used in epidemiology from a Bayesian viewpoint. It employs
the software package WinBUGS to carry out the analyses and offers
the code in the text and for download online. The book examines
study designs that investigate the association between exposure to
risk factors and the occurrence of disease. It covers introductory
adjustment techniques to compare mortality between states and
regression methods to study the association between various risk
factors and disease, including logistic regression, simple and
multiple linear regression, categorical/ordinal regression, and
nonlinear models. The text also introduces a Bayesian approach for
the estimation of survival by life tables and illustrates other
approaches to estimate survival, including a parametric model based
on the Weibull distribution and the Cox proportional hazards
(nonparametric) model. Using Bayesian methods to estimate the lead
time of the modality, the author explains how to screen for a
disease among individuals that do not exhibit any symptoms of the
disease. With many examples and end-of-chapter exercises, this book
is the first to introduce epidemiology from a Bayesian perspective.
It shows epidemiologists how these Bayesian models and techniques
are useful in studying the association between disease and exposure
to risk factors.
Using WinBUGS to implement Bayesian inferences of estimation and
testing hypotheses, Bayesian Methods for Measures of Agreement
presents useful methods for the design and analysis of agreement
studies. It focuses on agreement among the various players in the
diagnostic process. The author employs a Bayesian approach to
provide statistical inferences based on various models of intra-
and interrater agreement. He presents many examples that illustrate
the Bayesian mode of reasoning and explains elements of a Bayesian
application, including prior information, experimental information,
the likelihood function, posterior distribution, and predictive
distribution. The appendices provide the necessary theoretical
foundation to understand Bayesian methods as well as introduce the
fundamentals of programming and executing the WinBUGS software.
Taking a Bayesian approach to inference, this hands-on book
explores numerous measures of agreement, including the Kappa
coefficient, the G coefficient, and intraclass correlation. With
examples throughout and end-of-chapter exercises, it discusses how
to successfully design and analyze an agreement study.
Analyze Repeated Measures Studies Using Bayesian Techniques Going
beyond standard non-Bayesian books, Bayesian Methods for Repeated
Measures presents the main ideas for the analysis of repeated
measures and associated designs from a Bayesian viewpoint. It
describes many inferential methods for analyzing repeated measures
in various scientific areas, especially biostatistics. The author
takes a practical approach to the analysis of repeated measures. He
bases all the computing and analysis on the WinBUGS package, which
provides readers with a platform that efficiently uses prior
information. The book includes the WinBUGS code needed to implement
posterior analysis and offers the code for download online.
Accessible to both graduate students in statistics and consulting
statisticians, the book introduces Bayesian regression techniques,
preliminary concepts and techniques fundamental to the analysis of
repeated measures, and the most important topic for repeated
measures studies: linear models. It presents an in-depth
explanation of estimating the mean profile for repeated measures
studies, discusses choosing and estimating the covariance structure
of the response, and expands the representation of a repeated
measure to general mixed linear models. The author also explains
the Bayesian analysis of categorical response data in a repeated
measures study, Bayesian analysis for repeated measures when the
mean profile is nonlinear, and a Bayesian approach to missing
values in the response variable.
With Bayesian statistics rapidly becoming accepted as a way to
solve applied statisticalproblems, the need for a comprehensive,
up-to-date source on the latest advances in thisfield has
arisen.Presenting the basic theory of a large variety of linear
models from a Bayesian viewpoint,Bayesian Analysis of Linear Models
fills this need. Plus, this definitive volume containssomething
traditional-a review of Bayesian techniques and methods of
estimation, hypothesis,testing, and forecasting as applied to the
standard populations ... somethinginnovative-a new approach to
mixed models and models not generally studied by statisticianssuch
as linear dynamic systems and changing parameter models ... and
somethingpractical-clear graphs, eary-to-understand examples,
end-of-chapter problems, numerousreferences, and a distribution
appendix.Comprehensible, unique, and in-depth, Bayesian Analysis of
Linear Models is the definitivemonograph for statisticians,
econometricians, and engineers. In addition, this text isideal for
students in graduate-level courses such as linear models,
econometrics, andBayesian inference.
There are numerous advantages to using Bayesian methods in
diagnostic medicine, which is why they are employed more and more
today in clinical studies. Exploring Bayesian statistics at an
introductory level, Bayesian Biostatistics and Diagnostic Medicine
illustrates how to apply these methods to solve important problems
in medicine and biology. After focusing on the wide range of areas
where diagnostic medicine is used, the book introduces Bayesian
statistics and the estimation of accuracy by sensitivity,
specificity, and positive and negative predictive values for
ordinal and continuous diagnostic measurements. The author then
discusses patient covariate information and the statistical methods
for estimating the agreement among observers. The book also
explains the protocol review process for cancer clinical trials,
how tumor responses are categorized, how to use WHO and RECIST
criteria, and how Bayesian sequential methods are employed to
monitor trials and estimate sample sizes. With many tables and
figures, this book enables readers to conduct a Bayesian analysis
for a large variety of interesting and practical biomedical
problems.
Useful in many areas of medicine and biology, Bayesian methods
are particularly attractive tools for the design of clinical trials
and diagnostic tests, which are based on established information,
usually from related previous studies. Advanced Bayesian Methods
for Medical Test Accuracy begins with a review of the usual
measures such as specificity, sensitivity, positive and negative
predictive value, and the area under the ROC curve. Then the scope
expands to cover the more advanced topics of verification bias,
diagnostic tests with imperfect gold standards, and those for which
no gold standard is available.
Promoting accuracy and efficiency of clinical trials, tests, and
the diagnostic process, this book:
- Enables the user to efficiently apply prior information via a
WinBUGS package
- Presents many ideas for the first time and goes far beyond the
two standard references
- Integrates reader agreement with different modalities X-ray, CT
Scanners, and more to study their effect on medical test
accuracy
- Provides practical chapter-end problems
Useful for graduate students and consulting statisticians
working in the various areas of diagnostic medicine and study
design, this practical resource introduces the fundamentals of
programming and executing BUGS, giving readers the tools and
experience to successfully analyze studies for medical test
accuracy.
In many branches of science relevant observations are taken
sequentially over time. Bayesian Analysis of Time Series discusses
how to use models that explain the probabilistic characteristics of
these time series and then utilizes the Bayesian approach to make
inferences about their parameters. This is done by taking the prior
information and via Bayes theorem implementing Bayesian inferences
of estimation, testing hypotheses, and prediction. The methods are
demonstrated using both R and WinBUGS. The R package is primarily
used to generate observations from a given time series model, while
the WinBUGS packages allows one to perform a posterior analysis
that provides a way to determine the characteristic of the
posterior distribution of the unknown parameters. Features Presents
a comprehensive introduction to the Bayesian analysis of time
series. Gives many examples over a wide variety of fields including
biology, agriculture, business, economics, sociology, and
astronomy. Contains numerous exercises at the end of each chapter
many of which use R and WinBUGS. Can be used in graduate courses in
statistics and biostatistics, but is also appropriate for
researchers, practitioners and consulting statisticians. About the
author Lyle D. Broemeling, Ph.D., is Director of Broemeling and
Associates Inc., and is a consulting biostatistician. He has been
involved with academic health science centers for about 20 years
and has taught and been a consultant at the University of Texas
Medical Branch in Galveston, The University of Texas MD Anderson
Cancer Center and the University of Texas School of Public Health.
His main interest is in developing Bayesian methods for use in
medical and biological problems and in authoring textbooks in
statistics. His previous books for Chapman & Hall/CRC include
Bayesian Biostatistics and Diagnostic Medicine, and Bayesian
Methods for Agreement.
Written by a biostatistics expert with over 20 years of experience
in the field, Bayesian Methods in Epidemiology presents statistical
methods used in epidemiology from a Bayesian viewpoint. It employs
the software package WinBUGS to carry out the analyses and offers
the code in the text and for download online. The book examines
study designs that investigate the association between exposure to
risk factors and the occurrence of disease. It covers introductory
adjustment techniques to compare mortality between states and
regression methods to study the association between various risk
factors and disease, including logistic regression, simple and
multiple linear regression, categorical/ordinal regression, and
nonlinear models. The text also introduces a Bayesian approach for
the estimation of survival by life tables and illustrates other
approaches to estimate survival, including a parametric model based
on the Weibull distribution and the Cox proportional hazards
(nonparametric) model. Using Bayesian methods to estimate the lead
time of the modality, the author explains how to screen for a
disease among individuals that do not exhibit any symptoms of the
disease. With many examples and end-of-chapter exercises, this book
is the first to introduce epidemiology from a Bayesian perspective.
It shows epidemiologists how these Bayesian models and techniques
are useful in studying the association between disease and exposure
to risk factors.
This is the first book designed to introduce Bayesian inference
procedures for stochastic processes. There are clear advantages to
the Bayesian approach (including the optimal use of prior
information). Initially, the book begins with a brief review of
Bayesian inference and uses many examples relevant to the analysis
of stochastic processes, including the four major types, namely
those with discrete time and discrete state space and continuous
time and continuous state space. The elements necessary to
understanding stochastic processes are then introduced, followed by
chapters devoted to the Bayesian analysis of such processes. It is
important that a chapter devoted to the fundamental concepts in
stochastic processes is included. Bayesian inference (estimation,
testing hypotheses, and prediction) for discrete time Markov
chains, for Markov jump processes, for normal processes (e.g.
Brownian motion and the Ornstein-Uhlenbeck process), for
traditional time series, and, lastly, for point and spatial
processes are described in detail. Heavy emphasis is placed on many
examples taken from biology and other scientific disciplines. In
order analyses of stochastic processes, it will use R and WinBUGS.
Features: Uses the Bayesian approach to make statistical Inferences
about stochastic processes The R package is used to simulate
realizations from different types of processes Based on
realizations from stochastic processes, the WinBUGS package will
provide the Bayesian analysis (estimation, testing hypotheses, and
prediction) for the unknown parameters of stochastic processes To
illustrate the Bayesian inference, many examples taken from
biology, economics, and astronomy will reinforce the basic concepts
of the subject A practical approach is implemented by considering
realistic examples of interest to the scientific community WinBUGS
and R code are provided in the text, allowing the reader to easily
verify the results of the inferential procedures found in the many
examples of the book Readers with a good background in two areas,
probability theory and statistical inference, should be able to
master the essential ideas of this book.
Der Autor analysiert qualitativ und quantitativ die steuerlichen
Rahmenbedingungen fur die Forschungs- und Entwicklungstatigkeit
international agierender Konzerne und geht dabei der Frage nach,
inwieweit Unternehmen Steuerplanung durch die gezielte
Ausgestaltung von F&E betreiben koennen. Die Untersuchung legt
die gegenwartigen steuerlichen Chancen und Risiken
grenzuberschreitender Auftragsforschung offen und plausibilisiert
die Auswirkungen moeglicher Entwicklungen des Internationalen
Steuerrechts.
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