Statistical Rethinking
内容简介
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.
The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.
By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.
......(更多)
作者简介
Richard McElreath is the director of the Department of Human Behavior, Ecology, and Culture at the Max Planck Institute for Evolutionary Anthropology. He is also a professor in the Department of Anthropology at the University of California, Davis. His work lies at the intersection of evolutionary and cultural anthropology, specifically how the evolution of fancy social learning in humans accounts for the unusual nature of human adaptation and extraordinary scale and variety of human societies.
......(更多)
目录
Preface
Chapter 1: The Golem of Prague
Chapter 2: Small Worlds and Large Worlds
Chapter 3: Sampling the Imaginary
Chapter 4: Linear Models
Chapter 5: Multivariate Linear Models
Chapter 6: Overfitting and Model Comparison
Chapter 7: Interactions
Chapter 8: Markov chain Monte Carlo Estimation
Chapter 9: Big Entropy and the Generalized Linear Model
Chapter 10: Counting and Classification
Chapter 11: Monsters and Mixtures
Chapter 12: Multilevel Models
Chapter 13: Adventures in Covariance
Chapter 14: Missing Data and Other Opportunities
Chapter 15: Horoscopes
......(更多)
读书文摘
......(更多)
