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Bayesian Statistical Methods provides data scientists with the
foundational and computational tools needed to carry out a Bayesian
analysis. This book focuses on Bayesian methods applied routinely
in practice including multiple linear regression, mixed effects
models and generalized linear models (GLM). The authors include
many examples with complete R code and comparisons with analogous
frequentist procedures. In addition to the basic concepts of
Bayesian inferential methods, the book covers many general topics:
Advice on selecting prior distributions Computational methods
including Markov chain Monte Carlo (MCMC) Model-comparison and
goodness-of-fit measures, including sensitivity to priors
Frequentist properties of Bayesian methods Case studies covering
advanced topics illustrate the flexibility of the Bayesian
approach: Semiparametric regression Handling of missing data using
predictive distributions Priors for high-dimensional regression
models Computational techniques for large datasets Spatial data
analysis The advanced topics are presented with sufficient
conceptual depth that the reader will be able to carry out such
analysis and argue the relative merits of Bayesian and classical
methods. A repository of R code, motivating data sets, and complete
data analyses are available on the book's website. Brian J. Reich,
Associate Professor of Statistics at North Carolina State
University, is currently the editor-in-chief of the Journal of
Agricultural, Biological, and Environmental Statistics and was
awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh,
Professor of Statistics at North Carolina State University, has
over 22 years of research and teaching experience in conducting
Bayesian analyses, received the Cavell Brownie mentoring award, and
served as the Deputy Director at the Statistical and Applied
Mathematical Sciences Institute.
This volume describes how to conceptualize, perform, and critique
traditional generalized linear models (GLMs) from a Bayesian
perspective and how to use modern computational methods to
summarize inferences using simulation. Introducing dynamic modeling
for GLMs and containing over 1000 references and equations,
Generalized Linear Models considers parametric and semiparametric
approaches to overdispersed GLMs, presents methods of analyzing
correlated binary data using latent variables. It also proposes a
semiparametric method to model link functions for binary response
data, and identifies areas of important future research and new
applications of GLMs.
This volume describes how to conceptualize, perform, and critique
traditional generalized linear models (GLMs) from a Bayesian
perspective and how to use modern computational methods to
summarize inferences using simulation. Introducing dynamic modeling
for GLMs and containing over 1000 references and equations,
Generalized Linear Models considers parametric and semiparametric
approaches to overdispersed GLMs, presents methods of analyzing
correlated binary data using latent variables. It also proposes a
semiparametric method to model link functions for binary response
data, and identifies areas of important future research and new
applications of GLMs.
Bayesian Statistical Methods provides data scientists with the
foundational and computational tools needed to carry out a Bayesian
analysis. This book focuses on Bayesian methods applied routinely
in practice including multiple linear regression, mixed effects
models and generalized linear models (GLM). The authors include
many examples with complete R code and comparisons with analogous
frequentist procedures. In addition to the basic concepts of
Bayesian inferential methods, the book covers many general topics:
Advice on selecting prior distributions Computational methods
including Markov chain Monte Carlo (MCMC) Model-comparison and
goodness-of-fit measures, including sensitivity to priors
Frequentist properties of Bayesian methods Case studies covering
advanced topics illustrate the flexibility of the Bayesian
approach: Semiparametric regression Handling of missing data using
predictive distributions Priors for high-dimensional regression
models Computational techniques for large datasets Spatial data
analysis The advanced topics are presented with sufficient
conceptual depth that the reader will be able to carry out such
analysis and argue the relative merits of Bayesian and classical
methods. A repository of R code, motivating data sets, and complete
data analyses are available on the book's website. Brian J. Reich,
Associate Professor of Statistics at North Carolina State
University, is currently the editor-in-chief of the Journal of
Agricultural, Biological, and Environmental Statistics and was
awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh,
Professor of Statistics at North Carolina State University, has
over 22 years of research and teaching experience in conducting
Bayesian analyses, received the Cavell Brownie mentoring award, and
served as the Deputy Director at the Statistical and Applied
Mathematical Sciences Institute.
Metal-Organic Frameworks for Environmental Applications examines
this important topic, looking at potential materials and methods
for the remediation of pressing pollution issues, such as
heavy-metal contaminants in water streams, radioactive waste
disposal, marine oil-spillage, the treatment of textile and dye
industry effluents, the clean-up of trace amounts of explosives in
land and water, and many other topics. This survey of the
cutting-edge research and technology of MOFs is an invaluable
resource for researchers working in inorganic chemistry and
materials science, but it is also ideal for graduate students
studying MOFs and their applications.
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