STATISTICAL RETHINKING. A BAYESIAN COURSE WITH EXAMPLES IN R AND STAN. 2ND EDITION

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

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.
The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.
The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.

Features
• Integrates working code into the main text
• Illustrates concepts through worked data analysis examples
• Emphasizes understanding assumptions and how assumptions are reflected in code
• Offers more detailed explanations of the mathematics in optional sections
• Presents examples of using the dagitty R package to analyze causal graphs
• Provides the rethinking R package on the author's website and on GitHub