Statistical rethinking : a Bayesian course with examples in R and Stan
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The work Statistical rethinking : a Bayesian course with examples in R and Stan represents a distinct intellectual or artistic creation found in University of Missouri Libraries. This resource is a combination of several types including: Work, Language Material, Books.
The Resource
Statistical rethinking : a Bayesian course with examples in R and Stan
Resource Information
The work Statistical rethinking : a Bayesian course with examples in R and Stan represents a distinct intellectual or artistic creation found in University of Missouri Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Statistical rethinking : a Bayesian course with examples in R and Stan
 Title remainder
 a Bayesian course with examples in R and Stan
 Statement of responsibility
 Richard McElreath, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
 Subject

 BayesEntscheidungstheorie
 Bayesian statistical decision theory
 Bayesian statistical decision theory
 Electronic books
 Electronic books
 Electronic bookss
 MATHEMATICS / Applied
 MATHEMATICS / Applied
 MATHEMATICS / Probability & Statistics / General
 MATHEMATICS / Probability & Statistics / General
 R
 R
 R (Computer program language)
 R (Computer program language)
 Statistisches Modell
 Statistisches Modell
 BayesEntscheidungstheorie
 Language
 eng
 Summary
 Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers'knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today's modelbased statistics, the book pushes readers to perform stepbystep calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas
 Cataloging source
 N$T
 Dewey number
 519.5/42
 Illustrations
 illustrations
 Index
 index present
 LC call number
 QA279.5
 LC item number
 .M3975 2016eb
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 Series statement
 Chapman & Hall/CRC texts in statistical science series
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Context of Statistical rethinking : a Bayesian course with examples in R and StanWork of
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