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Frequently Asked Questions
The book utilizes R alongside the Stan programming language for Hamiltonian Monte Carlo sampling, using the author's custom 'rethinking' package.
It is written for graduate students, data scientists, quantitative researchers, and analysts in social, natural, and computer sciences looking to transition to Bayesian modeling.
The 2nd Edition features significantly expanded content on causal inference (DAGs), Gaussian processes, measurement error models, instrumental variables, and updated code implementations for Stan.
Yes. Causal inference and Directed Acyclic Graphs (DAGs) are integrated throughout the book to guide model specification and variable selection.
Yes. It is widely adopted as a primary textbook for university graduate seminars and advanced data science courses worldwide.
This is the official hardcover edition published by Chapman & Hall / CRC Press.
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