Free download and read ebooks online Statistical Rethinking - A Bayesian Course With Examples In R And STAN by Richard McElreath full book PDF ePUB Audiobook. Download a free ebooks and kindle audiobook Statistical Rethinking - A Bayesian Course With Examples In R And STAN with ISBN id 9780367139919 full page with book summary. Richard McElreath book PDF ePUB of Statistical Rethinking - A Bayesian Course With Examples In R And STAN published by CRC Press in 2020-03. Statistical Rethinking released with category Mathematics with total 594 pages.
Statistical Rethinking - A Bayesian Course With Examples In R And STAN by Richard McElreath free pdf book
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
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