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

فهرست مطالب :

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