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Description
Statistical Rethinking: A Bayesian Course with Examples in R and STAN (2nd Edition) presents a foundational computational approach to Bayesian data analysis and scientific modeling. Structured across 17 chapters, Richard McElreath guides readers from foundational probability and linear models to Markov chain Monte Carlo estimation. By integrating step-by-step code in R and the Stan probabilistic programming language, the text ensures researchers understand how model assumptions operate rather than relying on automated statistical routines.
This expanded second edition emphasizes directed acyclic graphs (DAGs) for causal inference, regularizing priors, and information-theoretic model comparison. Readers progress into generalized linear multilevel models, varying intercepts and slopes, Gaussian process regressions, and handling measurement error and missing data. Concluding with mechanistic modeling beyond standard generalized linear models, this Chapman & Hall/CRC volume equips quantitative practitioners with durable modeling skills.
Institutional Use
University departments in statistics, data science, ecology, cognitive science, and social sciences adopt this volume as a primary textbook for graduate-level and advanced undergraduate Bayesian modeling courses. Academic research libraries, quantitative method labs, and institutional research divisions maintain this hardback as an essential methodology reference for faculty, postdoctoral researchers, and doctoral candidates conducting reproducible empirical research and causal analysis with R and Stan.
Product Details
| ISBN-13 | 9780367139919 |
|---|---|
| ISBN-10 | 036713991X |
| Edition | 2nd Edition |
| Authors | Richard McElreath |
| Publisher | Chapman and Hall/CRC |
| Publication date | 2020-03-16 |
| Format | Hardcover |
| Language | English |
| Category | Physical Sciences & Mathematics |
| List price | $63.99 / copy |
| Min. order | 5 copies |
| Condition | New |
Table of Contents
- Chapter 1. The Golem of Prague
- Chapter 2. Small Worlds and Large Worlds
- Chapter 3. Sampling the Imaginary
- Chapter 4. Geocentric Models
- Chapter 5. The Many Variables & The Spurious Waffles
- Chapter 6. The Haunted DAG & The Causal Terror
- Chapter 7. Ulysses' Compass
- Chapter 8. Conditional Manatees
- Chapter 9. Markov Chain Monte Carlo
- Chapter 10. Big Entropy and the Generalized Linear Model
- Chapter 11. God Spiked the Integers
- Chapter 12. Monsters and Mixtures
- Chapter 13. Models with Memory
- Chapter 14. Adventures in Covariance
- Chapter 15. Missing Data and Other Opportunities
- Chapter 16. Generalized Linear Madness
- Chapter 17. Horoscopes
Key Topics
- Bayesian Data Analysis
- Causal Inference & Directed Acyclic Graphs (DAGs)
- Markov Chain Monte Carlo (MCMC) & Hamiltonian Monte Carlo
- Multilevel & Hierarchical Models
- Generalized Linear Models
- Information Criteria & Cross-Validation
- R & Stan Statistical Programming
- Measurement Error & Missing Data Imputation
Who This Is For
- Graduate Students in Statistics & Data Science
- Quantitative Researchers in Social, Behavioral & Natural Sciences
- University Faculty & Course Instructors
- Applied Statisticians & Research Methodologists
- Academic & Research Library Collections
Institutional & Bulk Ordering
- Minimum order: 5 copies, with tiered pricing at higher quantities
- Purchase orders and Net-30 terms accepted for qualifying institutions
- Tax-exempt certificates accepted for qualifying accounts
- Free worldwide shipping, no sales tax
Edition & Identifier Check
Always confirm this exact ISBN-13 and edition (9780367139919) against your syllabus or procurement request before ordering — editions of this title are not interchangeable.
Frequently Asked Questions
Q: What programming languages and computational tools are used in this textbook?
A: The book integrates hands-on scripts using the R statistical computing language alongside the Stan probabilistic programming language, supported by the author's rethinking package for model construction and diagnostic checks.
Q: What are the major structural additions in the second edition?
A: The second edition incorporates directed acyclic graphs (DAGs) throughout for causal inference, adds coverage of splines and ordered categorical predictors, expands on Hamiltonian Monte Carlo details, and adds a dedicated chapter on domain-specific mechanistic modeling beyond generalized linear models.
Q: What academic programs and disciplines commonly adopt this text?
A: It is widely adopted in graduate and advanced undergraduate curricula across statistics, data science, ecology, evolutionary anthropology, psychology, and quantitative social sciences.
Q: What is the physical format and page count of ISBN 9780367139919?
A: ISBN 9780367139919 corresponds to the hardback edition published by Chapman & Hall/CRC within the Texts in Statistical Science series.
Q: What is the minimum order quantity for this title?
A: The minimum order is 5 copies. Tiered pricing applies at 5, 10, and 25 copies; orders of 100 or more copies are quoted directly by email.
Q: Is this the correct edition for my program or syllabus?
A: This listing is ISBN 9780367139919 (ISBN-10 036713991X). Please confirm this exact identifier and edition against your syllabus or procurement request before ordering, as editions of this title are not interchangeable.
Q: Do you accept purchase orders and offer Net-30 terms?
A: Yes. Purchase orders and Net-30 terms are accepted for qualifying institutions, and tax-exempt certificates are accepted at checkout.
Q: Do you ship internationally?
A: Yes, Global Academic Supply offers free worldwide shipping with no sales tax on qualifying orders.