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Bayesian Data Analysis - Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin
Bayesian Data Analysis: A Comprehensive Guide to Bayesian Statistics
Introduction
Bayesian Data Analysis, Third Edition, by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin, is the definitive introduction to Bayesian statistics. The book provides a comprehensive coverage of Bayesian methods, from the basics to the most advanced techniques. It is written in a clear and accessible style, making it ideal for students, researchers, and practitioners alike.
Key Features
- Comprehensive coverage of Bayesian statistics, from the basics to the most advanced techniques
- Clear and accessible writing style, making it ideal for students, researchers, and practitioners alike
- Numerous examples and exercises to help readers understand and apply Bayesian methods
- Companion website with data sets, software, and other resources
What's New in the Third Edition
The third edition of Bayesian Data Analysis has been extensively revised and updated. Some of the key new features include:
- New chapters on Bayesian computation, model checking, and Bayesian nonparametrics
- Expanded coverage of hierarchical models, Markov chain Monte Carlo (MCMC) methods, and Bayesian model selection
- New sections on Bayesian approaches to big data, machine learning, and causal inference
- Updated examples and exercises throughout the book
- Companion website with new data sets, software, and other resources
Why You Should Read This Book
Bayesian Data Analysis is the essential guide to Bayesian statistics. It is a must-read for anyone who wants to understand and apply Bayesian methods to their research or practice.
Table of Contents
Part I: Introduction
- Chapter 1: Introduction to Bayesian Statistics
- Chapter 2: Probability and Bayes' Theorem
- Chapter 3: Bayesian Inference
Part II: Bayesian Computation
- Chapter 4: Markov Chain Monte Carlo (MCMC) Methods
- Chapter 5: Gibbs Sampling
- Chapter 6: Metropolis-Hastings Algorithm
- Chapter 7: Hamiltonian Monte Carlo (HMC)
Part III: Bayesian Models
- Chapter 8: Linear Regression
- Chapter 9: Logistic Regression
- Chapter 10: Bayesian Hierarchical Models
- Chapter 11: Bayesian Nonparametrics
Part IV: Bayesian Model Checking and Selection
- Chapter 12: Bayesian Model Checking
- Chapter 13: Bayesian Model Selection
Part V: Bayesian Applications
- Chapter 14: Bayesian Approaches to Big Data
- Chapter 15: Bayesian Machine Learning
- Chapter 16: Bayesian Causal Inference
Appendix
- Appendix A: Mathematical Background
- Appendix B: Software and Resources
Companion Website
The companion website for Bayesian Data Analysis, Third Edition, provides a wealth of additional resources, including:
- Data sets
- Software
- Code examples
- Exercises
- Solutions
Conclusion
Bayesian Data Analysis is the essential guide to Bayesian statistics. It is a must-read for anyone who wants to understand and apply Bayesian methods to their research or practice.