Introduction to Bayesian Statistics. Bayesian Hierarchical Modeling. Model-Based Bayesian Inference. Linear and Generalized Linear Models. Linear and Generalized Linear Mixed Models. Zero-Inflated Mixture Models. Survival Analysis. Nonparametric Regression and Additive Models. Functional Regression Models. Measurement Error Models. Quantile Regression.
Xiaofeng Wang is Professor of Medicine and Biostatistics at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and a Full Staff in the Department of Quantitative Health Sciences at Cleveland Clinic.
Yu Ryan Yue is Associate Professor of Statistics in the Paul H. Chook Department of Information Systems and Statistics at Baruch College, The City University of New York.
Julian J. Faraway is Professor of Statistics in the Department of Mathematical Sciences at the University of Bath.
"The book focuses on regression models with R-INLA and it will be
of interest to a wide audience. INLA is becoming a very popular
method for approximate Bayesian inference and it is being applied
to many problems in many different fields. This book will be of
interest not only to statisticians but also to applied researchers
in other disciplines interested in Bayesian inference. This book
can probably be used as a reference book for research and as a
textbook at graduate level."
~Virgilio Gómez-Rubio,University of Castilla-La Mancha"This is a
well-written book on an important subject, for which there is a
lack of good introductory material. The tutorial-style works
nicely, and they have an excellent set of examples. They manage to
do a practical introduction with just the right amount of theory
background…The book should be very useful to scientists who want to
analyze data using regression models. INLA allows users to fit
Bayesian models quickly and without too much programming effort,
and it has been used successfully in many applications. The book is
written in a tutorial style, while explaining the basics of the
needed theory very well, so it could serve both as a reference or
textbook…The book is well written and technically correct."
~Egil Ferkingstad, deCode genetics"The authors have done a great
job of not over-doing the technical details, thereby making the
presentation accessible to a broader audience beyond the statistics
world…It covers many contemporary parametric, nonparametric, and
semiparametric methods that applied scientists from many fields use
in modern research."
~Adam Branscum, Oregon State University
"The book focuses on regression models with R-INLA and it will be
of interest to a wide audience. INLA is becoming a very popular
method for approximate Bayesian inference and it is being applied
to many problems in many different fields. This book will be of
interest not only to statisticians but also to applied researchers
in other disciplines interested in Bayesian inference. This book
can probably be used as a reference book for research and as a
textbook at graduate level."
~Virgilio Gómez-Rubio, University of Castilla-La Mancha"This is a
well-written book on an important subject, for which there is a
lack of good introductory material. The tutorial-style works
nicely, and they have an excellent set of examples. They manage to
do a practical introduction with just the right amount of theory
background…The book should be very useful to scientists who want to
analyze data using regression models. INLA allows users to fit
Bayesian models quickly and without too much programming effort,
and it has been used successfully in many applications. The book is
written in a tutorial style, while explaining the basics of the
needed theory very well, so it could serve both as a reference or
textbook…The book is well written and technically correct."
~Egil Ferkingstad, deCode genetics"The authors have done a great
job of not over-doing the technical details, thereby making the
presentation accessible to a broader audience beyond the statistics
world…It covers many contemporary parametric, nonparametric, and
semiparametric methods that applied scientists from many fields use
in modern research."
~Adam Branscum, Oregon State University
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