Beizu Deta Kaiseki (Bayesian Data Analysis - 3rd edition)

The book 『ベイズデータ解析（第3版）』 is the Japanese translation of the globally renowned textbook "Bayesian Data Analysis (3rd Edition)." This book serves as a comprehensive guide to data analysis using Bayesian statistics, offering a systematic and thorough exploration of the theory and its applications. Bayesian statistics play an increasingly important role in modern statistics and data science, and this book is designed for a wide range of readers, from those new to Bayesian methods to experienced researchers and data scientists seeking to deepen their understanding and enhance their practical skills.
 
The book is structured to guide readers through the essential concepts of Bayesian statistics and their application in real-world data analysis. Part I begins with the foundational ideas of Bayesian statistics, providing a clear explanation of Bayes' theorem, prior probabilities, and posterior probabilities. These concepts are crucial for understanding the Bayesian approach, which is often contrasted with the frequentist perspective. By highlighting these differences, the book helps readers grasp how Bayesian inference and prediction are framed, laying the groundwork for more advanced topics.
 
From Part II onwards, it shifts focus to practical data analysis examples, illustrating how Bayesian methods are applied to real-world datasets. The book covers key models such as linear regression, generalized linear models, and hierarchical Bayesian models. Through these models, readers learn not only how to implement Bayesian analysis but also how to interpret the results in a meaningful way. The book also probes into computational methods for Bayesian analysis, such as Markov Chain Monte Carlo (MCMC) and variational inference, providing detailed guidance on how to use these methods for model estimation.
 
A distinctive feature of this book is its practical approach, which includes numerous examples of code written in R and Stan, popular software tools for statistical analysis. These examples allow readers to engage with the material actively, moving beyond theoretical understanding to developing practical skills in data analysis. This hands-on approach is further reinforced by the inclusion of exercises and case studies, enabling readers to apply what they have learned to solve real-world problems.
 
Bayesian statistics and data analysis based on its principles are increasingly being applied across various fields, from natural sciences to humanities and social sciences. The book also covers specific examples of these diverse applications, providing readers with a real sense of how Bayesian statistics can contribute to solving real-world problems.
 
Overall, the book provides an in-depth overview of Bayesian data analysis, making it an essential tool for students, researchers, and professionals who wish to grasp and implement Bayesian techniques in their work. With its clear explanations, practical illustrations, and focus on real-world applications, it is a vital resource for anyone engaged in contemporary data analysis.
 

(Written by KURISU Daisuke, Associate Professor, Center for Spatial Information Science / 2024)