book

Statistical Rethinking

Name: Statistical Rethinking
Author: Richard McElreath
ISBN: 9781482253481

by Richard McElreath

January 2018

Beginner to intermediate

487 pages

16h 46m

English

Chapman and Hall/CRC

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Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
AudienceTeaching strategyHow to use this bookInstalling the rethinking R packageAcknowledgments
Chapter 1. The Golem of Prague
1.1. Statistical golems1.2. Statistical rethinking1.3. Three tools for golem engineering1.4. Summary
Chapter 2. Small Worlds and Large Worlds
2.1. The garden of forking data2.2. Building a model2.3. Components of the model2.4. Making the model go2.5. Summary2.6. Practice
Chapter 3. Sampling the Imaginary
3.1. Sampling from a grid-approximate posterior3.2. Sampling to summarize3.3. Sampling to simulate prediction3.4. Summary3.5. Practice
Chapter 4. Linear Models
4.1. Why normal distributions are normal4.2. A language for describing models4.3. A Gaussian model of height4.4. Adding a predictor4.5. Polynomial regression4.6. Summary4.7. Practice

Chapter 5. Multivariate Linear Models
5.1. Spurious association5.2. Masked relationship5.3. When adding variables hurts5.4. Categorical variables5.5. Ordinary least squares and lm5.6. Summary5.7. Practice
Chapter 6. Overfitting, Regularization, and Information Criteria
6.1. The problem with parameters6.2. Information theory and model performance6.3. Regularization6.4. Information criteria6.5. Using information criteria6.6. Summary6.7. Practice
Chapter 7. Interactions
7.1. Building an interaction7.2. Symmetry of the linear interaction7.3. Continuous interactions7.4. Interactions in design formulas7.5. Summary7.6. Practice
Chapter 8. Markov Chain Monte Carlo
8.1. Good King Markov and His island kingdom8.2. Markov chain Monte Carlo8.3. Easy HMC: map2stan8.4. Care and feeding of your Markov chain8.5. Summary8.6. Practice
Chapter 9. Big Entropy and the Generalized Linear Model
9.1. Maximum entropy9.2. Generalized linear models9.3. Maximum entropy priors9.4. Summary
Chapter 10. Counting and Classification
10.1. Binomial regression10.2. Poisson regression10.3. Other count regressions10.4. Summary10.5. Practice
Chapter 11. Monsters and Mixtures
11.1. Ordered categorical outcomes11.2. Zero-inflated outcomes11.3. Over-dispersed outcomes11.4. Summary11.5. Practice
Chapter 12. Multilevel Models
12.1. Example: Multilevel tadpoles12.2. Varying effects and the underfitting/overfitting trade-off12.3. More than one type of cluster12.4. Multilevel posterior predictions12.5. Summary12.6. Practice
Chapter 13. Adventures in Covariance
13.1. Varying slopes by construction13.2. Example: Admission decisions and gender13.3. Example: Cross-classified chimpanzees with varying slopes13.4. Continuous categories and the Gaussian process13.5. Summary13.6. Practice
Chapter 14. Missing Data and Other Opportunities
14.1. Measurement error14.2. Missing data14.3. Summary14.4. Practice
Chapter 15. Horoscopes
Endnotes
Bibliography
Citation index
Topic index

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