Statistical rethinking : a Bayesian course with examples in R and Stan
著者
書誌事項
Statistical rethinking : a Bayesian course with examples in R and Stan
（Texts in statistical science）
CRC Press, 2020
2nd ed
- : hbk
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注記
Includes bibliographical references (p. 573-583) and indexes
内容説明・目次
内容説明
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.
The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.
The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.
Features
Integrates working code into the main text
Illustrates concepts through worked data analysis examples
Emphasizes understanding assumptions and how assumptions are reflected in code
Offers more detailed explanations of the mathematics in optional sections
Presents examples of using the dagitty R package to analyze causal graphs
Provides the rethinking R package on the author's website and on GitHub
目次
1. The Golem of Prague. 2. Small Worlds and Large Worlds. Chapter 3. Sampling the Imaginary. 4. Geocentric Models. 5. The Many Variables & The Spurious Waffles. 6. The Haunted DAG & The Causal Terror. 7. Ulysses' Compass. 8. Conditional Manatees. 8. Conditional Manatees. 9. Markov Chain Monte Carlo. 10. Big Entropy and the Generalized Linear Model. 11. God Spiked the Integers. 12. Monsters and Mixtures. 13. Models With Memory. 14. Adventures in Covariance. 15. Missing Data and Other Opportunities. 16. Generalized Linear Madness. 17. Horoscopes.
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