دانلود کتاب Statistical Rethinking

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Preface Audience Teaching strategy How to use this book Installing the rethinking R package Acknowledgments 1: The Golem of Prague 1.1. Statistical golems 1.2. Statistical rethinking 1.3. Three tools for golem engineering 1.4. Summary 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 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 4: Linear Models 4.1. Why normal distributions are normal 4.2. A language for describing models 4.3. A Gaussian model of height 4.4. Adding a predictor 4.5. Polynomial regression 4.6. Summary 4.7. Practice 5: Multivariate Linear Models 5.1. Spurious association 5.2. Masked relationship 5.3. When adding variables hurts 5.4. Categorical variables 5.5. Ordinary least squares and lm 5.6. Summary 5.7. Practice 6: Overfitting, Regularization, and Information Criteria 6.1. The problem with parameters 6.2. Information theory and model performance 6.3. Regularization 6.4. Information criteria 6.5. Using information criteria 6.6. Summary 6.7. Practice 7: Interactions 7.1. Building an interaction 7.2. Symmetry of the linear interaction 7.3. Continuous interactions 7.4. Interactions in design formulas 7.5. Summary 7.6. Practice 8: Markov Chain Monte Carlo 8.1. Good King Markov and His island kingdom 8.2. Markov chain Monte Carlo 8.3. Easy HMC: map2stan 8.4. Care and feeding of your Markov chain 8.5. Summary 8.6. Practice 9: Big Entropy and the Generalized Linear Model 9.1. Maximum entropy 9.2. Generalized linear models 9.3. Maximum entropy priors 9.4. Summary 10: Counting and Classification 10.1. Binomial regression 10.2. Poisson regression 10.3. Other count regressions 10.4. Summary 10.5. Practice 11: Monsters and Mixtures 11.1. Ordered categorical outcomes 11.2. Zero-inflated outcomes 11.3. Over-dispersed outcomes 11.4. Summary 11.5. Practice 12: Multilevel Models 12.1. Example: Multilevel tadpoles 12.2. Varying effects and the underfitting/overfitting trade-off 12.3. More than one type of cluster 12.4. Multilevel posterior predictions 12.5. Summary 12.6. Practice 13: Adventures in Covariance 13.1. Varying slopes by construction 13.2. Example: Admission decisions and gender 13.3. Example: Cross-classified chimpanzees with varying slopes 13.4. Continuous categories and the Gaussian process 13.5. Summary 13.6. Practice 14: Missing Data and Other Opportunities 14.1. Measurement error 14.2. Missing data 14.3. Summary 14.4. Practice 15: Horoscopes Endnotes Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Bibliography Citation index Topic index