Paperback : HK$407.00
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:
Case studies covering advanced topics illustrate the flexibility of the Bayesian approach:
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.
Show moreBayesian 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:
Case studies covering advanced topics illustrate the flexibility of the Bayesian approach:
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.
Show more1. Introduction to Bayesian Inferential Framework. 2. Prior Knowledge to Posterior Inference. 3. Computational Methods. 4. Linear and Generalized Linear Regression Methods. 5. Models for Large Dimensional Parameters. 6. Models for Dependent Data. 7. Models for Data with Irregularities. 8. Models for Infinite Dimensional Parameters. 9. Advanced Computational Methods. 10. Case Studies Using Advanced Bayesian Methods
The code and data is at https://bayessm.wordpress.ncsu.edu/.
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
"Brian J. Reich and Sujit K. Ghosh make a valuable contribution to
the growing canon of introductory texts on Bayesian statistics…The
extensive data and problem sets provided are a major highlight of
the work…Features that instructors will find quite appealing
include the nice library of problem sets (with solutions to odd
problems in chapters 1-5 online), the availability online of
several nice worked data examples including code, and coverage of
some topics not yet standard in introductory texts, including
Bayesian computation with big data…A big plus is the recent
addition of Python code (PyMC) online…Because several of the
exercises are application based and incorporate data from a variety
of disciplines, the book will surely capture the interest of its
intended readership."
~Biometrics"A book that gives a comprehensive coverage of Bayesian
inference for a diverse background of scientific practitioners is
needed. The book Bayesian Statistical Methods seems to be a good
candidate for this purpose, which aims at a balanced treatment
between theory and computation. The authors are leading researchers
and experts in Bayesian statistics. I believe this book is likely
to be an excellent text book for an introductory course targeting
at first-year graduate students or undergraduate statistics
majors…This new book is more focused on the most fundamental
components of Bayesian methods. Moreover, this book contains many
simulated examples and real-data applications, with computer code
provided to demonstrate the implementations."
~Qing Zhou, UCLA"The book gives an overview of Bayesian statistical
modeling with a focus on the building blocks for fitting and
analyzing hierarchical models. The book uses a number of
interesting and realistic examples to illustrate the methods. The
computational focus is in the use of JAGS, as a tool to perform
Bayesian inference using Markov chain Monte Carlo methods…It can be
targeted as a textbook for upper-division undergraduate students in
statistics and some areas of science, engineering and social
sciences with an interest in a reasonably formal development of
data analytic methods and uncertainty quantification. It could also
be used for a Master’s class in statistical modeling."
~Bruno Sansó, University of California Santa Cruz"The given
manuscript sample is technically correct, clearly written, and at
an appropriate level of difficulty… I enjoyed the real-life
problems in the Chapter 1 exercises. I especially like the problem
on the Federalist Papers, because the students can revisit this
problem and perform more powerful inferences using the advanced
Bayesian methods that they will learn later in the textbook… I
would seriously consider adopting the book as a required textbook.
This text provides more details, R codes, and illuminating
visualizations compared to competing books, and more quickly
introduces a broad scope of regression models that are important in
practical applications."
~Arman Sabbaghi, Purdue University"The authors are leading
researchers and experts in Bayesian statistics. I believe this book
is likely to be an excellent textbook for an introductory course
targeting at first-year graduate students or
undergraduate statistics majors..."
~Qing Zhou, UCLA "I would seriously consider adopting the book as a
required textbook. This text provides more details, R codes, and
illuminating visualizations compared to competing books, and more
quickly introduces a broad scope of regression models that are
important in practical applications…"
~Arman Sabbaghi, Purdue University"The book gives an overview of
Bayesian statistical modeling with a focus on the building blocks
for fitting and analyzing hierarchical models. The book uses a
number of interesting and realistic examples to illustrate the
methods. The computational focus is in the use of JAGS, as a tool
to perform Bayesian inference using Markov chain Monte Carlo
methods…It can be targeted as a textbook for upper-division
undergraduate students in statistics and some areas of science,
engineering and social sciences with an interest in a reasonably
formal development of data analytic methods and uncertainty
quantification. It could also be used for a Master’s class in
statistical modeling."
~Bruno Sansó, University of California Santa Cruz
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