000			03409 a2200181 4500
020			_a9781482253443
082			_a519.542 _bMcE
100			_aMcElreath, Richard _946880
245			_aStatistical rethinking : _ba bayesian course with examples in R and Stan
260			_bCRC Press _c2016 _aBoca Raton
300			_axvii,469p.
440			_aTexts in statistical science series Ed. by Francesca Dominici & Others _946800
505			_aCONTENTS Preface xi Audience xi Teaching strategy xii How to use this book xii Installing the rethinking R package xvi Acknowledgments xvi Chapter 1. The Golem of Prague 1 Statistical golems 1 Statistical rethinking 4 Three tools for golem engineering 10 Summary 16 Chapter 2. Small Worlds and Large Worlds 19 The garden of forking data 20 Building a model 28 Components of the model 32 Making the model go 37 Summary 45 Practice 45 Chapter 3. Sampling the Imaginary 49 Sampling from a grid-approximate posterior 52 Sampling to summarize 53 Sampling to simulate prediction 61 Summary 68 Practice 69 Chapter 4. Linear Models 71 Why normal distributions are normal 72 A language for describing models 77 A Gaussian model of height 78 Adding a predictor 92 Polynomial regression 110 Summary 115 Practice 115 Chapter 5. Multivariate Linear Models 119 Spurious association 121 Masked relationship 135 When adding variables hurts 141 Categorical variables 152 Ordinary least squares and 1m 159 Summary 162 Practice 162 Chapter 6. Overfitting, Regularization, and Information Criteria 165 The problem with parameters 167 Information theory and model performance 174 Regularization 186 Information criteria 188 Using information criteria 195 Summary 205 Practice 205 Chapter 7. Interactions 209 Building an interaction 211 Symmetry of the linear interaction 223 Continuous interactions 225 Interactions in design formulas 235 Summary 236 Practice 236 Chapter 8. Markov Chain Monte Carlo 241 Good King Markov and His island kingdom 242 Markov chain Monte Carlo 245 Easy HMC: map2stan 247 Care and feeding of your Markov chain 255 Summary 263 Practice 263 Chapter 9. Big Entropy and the Generalized Linear Model 267 Maximum entropy 268 Generalized linear models 280 Maximum entropy priors 288 Summary 289 Chapter 10. Counting and Classification 291 Binomial regression 292 Poisson regression 311 Other count regressions 322 Summary 328 Practice 329 Chapter 11. Monsters and Mixtures 331 Ordered categorical outcomes 331 Zero-inflated outcomes 342 Over-dispersed outcomes 346 Summary , 351 Practice 352 Chapter 12. Multilevel Models 355 Example: Multilevel tadpoles 357 Varying effects and the under fitting/overfitting trade-off 364 More than one type of cluster 370 Multilevel posterior predictions 376 Summary 384 Practice 384 Chapter 13. Adventures in Covariance 387 Varying slopes by construction 389 Example: Admission decisions and gender 398 Example: Cross-classified chimpanzees with varying slopes 403 Continuous categories and the Gaussian process 410 Summary 419 Practice 419 Chapter 14. Missing Data and Other Opportunities 423 Measurement error 424 Missing data 431 Summary 439 Practice 439 Chapter 15. Horoscopes 441 Endnotes 445 Bibliography 457 Citation index 465 Topic index 467
600			_946798
890			_aUSA
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