Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)

Cover
Half Title
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