(Chapman & Hall/CRC Texts in Statistical Science) 2nd Edition
by Richard McElreath (Author)
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
