Download Instructor Resources of Statistical Rethinking: A Bayesian Course with Examples in R and STAN, 2nd Edition - Instructor's Solutions Manual
PDF Please note that by purchasing this product, you will only receive instructor resources related to the book. This product does not include the book itself. by Richard McElreath
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Format: PDF Language: English Edition: 2 Publisher: Chapman and Hall/CRC (March 16, 2020) Publication Date of the Electronic Edition: 2020
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ISBN: 9780367139919, 036713991X, 9780429639142, 0429639147, 9780429029608, 9780429635977, 9780429642319
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Description
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.FeaturesIntegrates working code into the main textIllustrates concepts through worked data analysis examplesEmphasizes understanding assumptions and how assumptions are reflected in codeOffers more detailed explanations of the mathematics in optional sectionsPresents examples of using the dagitty R package to analyze causal graphsProvides the rethinking R package on the author's website and on GitHub.
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