Foundations of Bayesian Statistics for Data Scientists
With R and Python
1st Edition
Will be published approx. on 9. June 2026
450 pages
978-1-040-84524-0 (ISBN)
Description
This book is an overview of the Bayesian approach to applying the most important inferential methods of statistical science. It is designed as a textbook for advanced undergraduate and master's students in Data Science, Statistics, or Mathematics who are interested in learning about Bayesian statistics.
The reader should be familiar with calculus and should have taken a statistical inference Statistics course covering the basic rules of probability, probability distributions and expectations, as well as the fundamentals of the traditional, frequentist approach to statistics, including sampling distributions, likelihood functions, basic inferential methods such as point estimation, confidence intervals, significance tests, and linear regression models.
Key Features:
? Uses real world data examples and contains numerous exercises.
? Includes software appendices in R and Python.
? Offers slides, labs, and other materials on the book's website.
Each chapter begins with a brief review of the primary frequentist methods for its topic before introducing corresponding Bayesian methods. This book presents some substantive theory as well as the methods, and is therefore intended for a reader who wishes to understand Bayesian methods rather than merely apply them. The focus is not just on presenting statistical methodologies but also on demonstrating how to implement them with modern software, emphasizing appropriate simulation methods.
The reader should be familiar with calculus and should have taken a statistical inference Statistics course covering the basic rules of probability, probability distributions and expectations, as well as the fundamentals of the traditional, frequentist approach to statistics, including sampling distributions, likelihood functions, basic inferential methods such as point estimation, confidence intervals, significance tests, and linear regression models.
Key Features:
? Uses real world data examples and contains numerous exercises.
? Includes software appendices in R and Python.
? Offers slides, labs, and other materials on the book's website.
Each chapter begins with a brief review of the primary frequentist methods for its topic before introducing corresponding Bayesian methods. This book presents some substantive theory as well as the methods, and is therefore intended for a reader who wishes to understand Bayesian methods rather than merely apply them. The focus is not just on presenting statistical methodologies but also on demonstrating how to implement them with modern software, emphasizing appropriate simulation methods.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Product notice
Reflowable
Illustrations
22 Tables, black and white; 67 Line drawings, color; 3 Line drawings, black and white; 67 Illustrations, color; 3 Illustrations, black and white
File size
9,72 MB
ISBN-13
978-1-040-84524-0 (9781040845240)
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
Additional editions
Alan Agresti | Maria Kateri | Ranjini Grove
Foundations of Bayesian Statistics for Data Scientists
With R and Python
Book
approx. 06/2026
1st Edition
CRC Press
€74.50
Not yet published
Alan Agresti | Maria Kateri | Ranjini Grove
Foundations of Bayesian Statistics for Data Scientists
With R and Python
Book
approx. 06/2026
1st Edition
CRC Press
€185.50
Not yet published
Persons
Alan Agresti, Distinguished Professor Emeritus at the University of Florida, is the author of seven books, and has presented short courses in 35 countries. His awards include an honorary doctorate from De Montfort University (UK) and Statistician of the Year from the American Statistical Association (Chicago chapter).
Maria Kateri, Professor of Statistics and Data Science at the RWTH Aachen University. She has long-term experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, Business Administration, and Engineering.
Ranjini Grove is a Teaching Professor in the Department of Statistics at the University of Washington. Since receiving her doctoral degree at Cornell University, she has also held faculty appointments at Brown University and at the University of Florida. In 2022 she was a finalist for a distinguished teaching award at the University of Washington. Since taking a break from academia to be a stay-at-home mom, she has been devoting much energy towards creating an inclusive, learner-centered, and engaging classroom.
Antonietta Mira is a Professor of Statistics at the Universita della Svizzera italiana and Insubria University. She is a Fellow of both the International Society for Bayesian Analysis and the Institute of Mathematical Statistics, elected member of the International Statistical Institute, and a recipient of the Savage Award for outstanding doctoral dissertations in Bayesian econometrics and statistics. Her research focuses on Bayesian learning and computing, with a strong interdisciplinary approach. She is also passionate about science communication through books, exhibitions, and mathematical magic conference shows.
Maria Kateri, Professor of Statistics and Data Science at the RWTH Aachen University. She has long-term experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, Business Administration, and Engineering.
Ranjini Grove is a Teaching Professor in the Department of Statistics at the University of Washington. Since receiving her doctoral degree at Cornell University, she has also held faculty appointments at Brown University and at the University of Florida. In 2022 she was a finalist for a distinguished teaching award at the University of Washington. Since taking a break from academia to be a stay-at-home mom, she has been devoting much energy towards creating an inclusive, learner-centered, and engaging classroom.
Antonietta Mira is a Professor of Statistics at the Universita della Svizzera italiana and Insubria University. She is a Fellow of both the International Society for Bayesian Analysis and the Institute of Mathematical Statistics, elected member of the International Statistical Institute, and a recipient of the Savage Award for outstanding doctoral dissertations in Bayesian econometrics and statistics. Her research focuses on Bayesian learning and computing, with a strong interdisciplinary approach. She is also passionate about science communication through books, exhibitions, and mathematical magic conference shows.
Content
1. Introduction to Bayesian Statistics 2. Bayesian Inference for Proportions 3. Bayesian Inference for Means 4. Bayesian Inference for Linear Models 5. Bayesian Inference for Generalized Linear Models 6. Bayesian MCMC Posterior Computation and Diagnostics 7. Choosing and Extending Bayesian Models Appendix A Using R for Bayesian Data Analysis Appendix Appendix B Using Python in Statistical Science Appendix C Solutions to Exercises (odd-numbered)
