Bayesian Statistical Methods
1st Edition
Published on 16. April 2019
Book
Hardback
288 pages
978-0-8153-7864-8 (ISBN)
Description
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.
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.
Reviews / Votes
"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 Sanso, 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 Sanso, University of California Santa Cruz) "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 Sanso, 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 Sanso, University of California Santa Cruz)
More details
Series
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Postgraduate
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 20 mm
Weight
602 gr
ISBN-13
978-0-8153-7864-8 (9780815378648)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Book
approx. 02/2026
2nd Edition
Chapman & Hall/CRC
€134.10
Not yet published
Additional editions
Brian J. Reich | Sujit K. Ghosh
Bayesian Statistical Methods
Book
06/2021
1st Edition
Chapman & Hall/CRC
€60.00
Shipment within 10-20 days
Persons
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
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
Content
1. 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/.
The code and data is at https://bayessm.wordpress.ncsu.edu/.