Efnisyfirlit

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• Preface to the Second Edition
• Preface
• Audience
• Teaching strategy
• How to use this book
• Installing the rethinking R package
• Acknowledgments
• Chapter 1. The Golem of Prague
• 1.1. Statistical golems
• 1.2. Statistical rethinking
• 1.3. Tools for golem engineering
• 1.4. Summary
• Chapter 2. Small Worlds and Large Worlds
• 2.1. The garden of forking data
• 2.2. Building a model
• 2.3. Components of the model
• 2.4. Making the model go
• 2.5. Summary
• 2.6. Practice
• Chapter 3. Sampling the Imaginary
• 3.1. Sampling from a grid-approximate posterior
• 3.2. Sampling to summarize
• 3.3. Sampling to simulate prediction
• 3.4. Summary
• 3.5. Practice
• Chapter 4. Geocentric Models
• 4.1. Why normal distributions are normal
• 4.2. A language for describing models
• 4.3. Gaussian model of height
• 4.4. Linear prediction
• 4.5. Curves from lines
• 4.6. Summary
• 4.7. Practice
• Chapter 5. The Many Variables & The Spurious Waffles
• 5.1. Spurious association
• 5.2. Masked relationship
• 5.3. Categorical variables
• 5.4. Summary
• 5.5. Practice
• Chapter 6. The Haunted DAG & The Causal Terror
• 6.1. Multicollinearity
• 6.2. Post-treatment bias
• 6.3. Collider bias
• 6.4. Confronting confounding
• 6.5. Summary
• 6.6. Practice
• Chapter 7. Ulysses’ Compass
• 7.1. The problem with parameters
• 7.2. Entropy and accuracy
• 7.3. Golem taming: regularization
• 7.4. Predicting predictive accuracy
• 7.5. Model comparison
• 7.6. Summary
• 7.7. Practice
• Chapter 8. Conditional Manatees
• 8.1. Building an interaction
• 8.2. Symmetry of interactions
• 8.3. Continuous interactions
• 8.4. Summary
• 8.5. Practice
• Chapter 9. Markov Chain Monte Carlo
• 9.1. Good King Markov and his island kingdom
• 9.2. Metropolis algorithms
• 9.3. Hamiltonian Monte Carlo
• 9.4. Easy HMC: ulam
• 9.5. Care and feeding of your Markov chain
• 9.6. Summary
• 9.7. Practice
• Chapter 10. Big Entropy and the Generalized Linear Model
• 10.1. Maximum entropy
• 10.2. Generalized linear models
• 10.3. Maximum entropy priors
• 10.4. Summary
• Chapter 11. God Spiked the Integers
• 11.1. Binomial regression
• 11.2. Poisson regression
• 11.3. Multinomial and categorical models
• 11.4. Summary
• 11.5. Practice
• Chapter 12. Monsters and Mixtures
• 12.1. Over-dispersed counts
• 12.2. Zero-inflated outcomes
• 12.3. Ordered categorical outcomes
• 12.4. Ordered categorical predictors
• 12.5. Summary
• 12.6. Practice
• Chapter 13. Models With Memory
• 13.1. Example: Multilevel tadpoles
• 13.2. Varying effects and the underfitting/overfitting trade-off
• 13.3. More than one type of cluster
• 13.4. Divergent transitions and non-centered priors
• 13.5. Multilevel posterior predictions
• 13.6. Summary
• 13.7. Practice
• Chapter 14. Adventures in Covariance
• 14.1. Varying slopes by construction
• 14.2. Advanced varying slopes
• 14.3. Instruments and causal designs
• 14.4. Social relations as correlated varying effects
• 14.5. Continuous categories and the Gaussian process
• 14.6. Summary
• 14.7. Practice
• Chapter 15. Missing Data and Other Opportunities
• 15.1. Measurement error
• 15.2. Missing data
• 15.3. Categorical errors and discrete absences
• 15.4. Summary
• 15.5. Practice
• Chapter 16. Generalized Linear Madness
• 16.1. Geometric people
• 16.2. Hidden minds and observed behavior
• 16.3. Ordinary differential nut cracking
• 16.4. Population dynamics
• 16.5. Summary
• 16.6. Practice
• Chapter 17. Horoscopes
• Endnotes
• Bibliography
• Citation index
• Topic index