{"product_id":"statistical-rethinking-a-bayesian-course-with-examples-in-r-and-stan-9780367139919","title":"Statistical Rethinking: A Bayesian Course with Examples in R and Stan","description":"\u003cp\u003e\u003cb\u003e\u003cstrong\u003eWinner of the 2024 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)\u003c\/strong\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eStatistical Rethinking: A Bayesian Course with Examples in R and Stan\u003c\/b\u003e 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.\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFeatures\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e \u003cul\u003e \u003cli\u003eIntegrates working code into the main text.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/ul\u003e\u003cul\u003e \u003cul\u003e \u003cli\u003eIllustrates concepts through worked data analysis examples.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/ul\u003e\u003cul\u003e \u003cul\u003e \u003cli\u003eEmphasizes understanding assumptions and how assumptions are reflected in code.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/ul\u003e\u003cul\u003e \u003cul\u003e \u003cli\u003eOffers more detailed explanations of the mathematics in optional sections.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/ul\u003e\u003cul\u003e \u003cul\u003e \u003cli\u003ePresents examples of using the dagitty R package to analyze causal graphs. \u003cp\u003e\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003eProvides the rethinking R package on the author's website and on GitHub.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eRichard McElreath\u003c\/strong\u003e 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), \u003ci\u003eMathematical Models of Social Evolution\u003c\/i\u003e.\u003c\/p\u003e\u003cbr\u003e","brand":"CRC Press","offers":[{"title":"Default Title","offer_id":50874392281362,"sku":"9780367139919","price":113.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_3890aab7-0890-42c0-875d-1e960b551656.jpg?v=1737991943","url":"https:\/\/surprise-castle.myshopify.com\/products\/statistical-rethinking-a-bayesian-course-with-examples-in-r-and-stan-9780367139919","provider":"Surprise Castle","version":"1.0","type":"link"}