Description
Statistical Rethinking (2nd Ed.)
A Bayesian Course with Examples in R and STAN
Chapman & Hall/CRC Texts in Statistical Science Series
Author: McElreath Richard
Language: EnglishSubjects for Statistical Rethinking:
Keywords
Posterior Distribution; Posterior Predictions; statistical modeling; Posterior Predictive Distribution; generalized linear multilevel models; Bayesian Data Analysis; maximum entropy; Grid Approximation; Bayesian probability; Quadratic Approximation; Gaussian process models; GLM; measurement error; Varying Intercepts; rethinking R package; Flat Priors; statistical inference in the natural and social sciences; Predictor Variables; model-based statistics; Terrain Ruggedness; Markov chain Monte Carlo; Waffle Houses; Multivariate Linear Models; Kl Divergence; statistical rethinking; Gamma Poisson Model; instrumental variables; MCMC; social relations models; Non-centered Parameterization; directed acyclic graph approach; Multilevel Models; Bayesian course; Log Gdp; Adaptive Priors; Divergent Transitions; Data Frame; Milk Energy; Missing Values; Brain Size; Prior Predictive Distribution
· 17.8x25.4 cm · Hardback
Description
/li>Contents
/li>Biography
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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
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.
Richard McElreath studies human evolutionary ecology and is a Director at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. He has published extensively on the mathematical theory and statistical analysis of social behavior, including his first book (with Robert Boyd), Mathematical Models of Social Evolution.