Statistical Rethinking: A Bayesian Course with Examples (2nd Edition)

By Richard McElreath

Statistical Rethinking, Second Edition, is a modern and intuitive introduction to Bayesian data analysis. Published by CRC Press (Chapman & Hall/CRC), this widely acclaimed textbook emphasizes conceptual understanding, causal reasoning, and practical model-building using real-world examples.

The 2nd Edition expands coverage of Bayesian inference, multilevel models, causal diagrams, and computational tools using R and Stan. Designed to build statistical thinking from first principles, it is widely used in data science, statistics, and social science graduate programs.

What This Book Does

This book equips students and researchers with the foundations of Bayesian statistics, emphasizing model construction, causal inference, and predictive analysis rather than formula memorization.

Key Features of the 2nd Edition

• Concept-driven introduction to Bayesian inference
• Extensive examples using R and Stan
• Coverage of multilevel and hierarchical models
• Emphasis on causal diagrams and model comparison
• Accessible explanations with real-world applications

Who Should Use This Book?

• Graduate students in statistics and data science
• Researchers in social and behavioral sciences
• Data analysts and quantitative researchers
• Faculty teaching Bayesian methods
• Advanced undergraduate statistics students

Why It’s Essential

• Widely regarded modern Bayesian statistics textbook
• Bridges theory with computational practice
• Focuses on understanding over memorization
• Trusted in graduate-level quantitative programs

A definitive guide to Bayesian statistical thinking and modeling.
Order today from BooksGoat and master modern Bayesian analysis.

Product Details

• ISBN-13: 9780367139919
• Edition: 2nd
• Author: Richard McElreath
• Publisher: CRC Press (Chapman & Hall/CRC)
• Format: Hardcover
• Condition: New
• Availability: In Stock
• Price: $— (Free Shipping)

Table of Contents (Highlights)

• Bayesian Foundations
• Probability and Inference
• Linear and Multilevel Models
• Model Comparison and Validation
• Causal Inference and DAGs
• Computational Modeling with R and Stan

FAQs

• Is this suitable for beginners?
  It assumes basic statistics knowledge but builds Bayesian reasoning from first principles.
• Does it include computational tools?
  Yes. It includes examples using R and Stan.
• Is it widely used in graduate programs?
  Yes. It is a highly regarded modern Bayesian statistics text.