The Resource Statistical rethinking : a Bayesian course with examples in R and Stan, Richard McElreath, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
Statistical rethinking : a Bayesian course with examples in R and Stan, Richard McElreath, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
Resource Information
The item Statistical rethinking : a Bayesian course with examples in R and Stan, Richard McElreath, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item Statistical rethinking : a Bayesian course with examples in R and Stan, Richard McElreath, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
 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
 Language
 eng
 Extent
 1 online resource (xvii, 469 pages)
 Note
 "A CRC title."
 Contents

 The golem of Prague
 Small worlds and large worlds
 Sampling the imaginary
 Linear models
 Multivariate linear models
 Overfitting, regularization, and information criteria
 Interactions
 Markov chain Monte Carlo
 Big entropy and the generalized linear model
 Counting and classification
 Monsters and mixtures
 Multilevel models
 Adventures in covariance
 Missing data and other opportunities
 Horoscopes
 Isbn
 9781482253467
 Label
 Statistical rethinking : a Bayesian course with examples in R and Stan
 Title
 Statistical rethinking
 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
 http://library.link/vocab/creatorDate
 1973
 http://library.link/vocab/creatorName
 McElreath, Richard
 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
 http://library.link/vocab/subjectName

 Bayesian statistical decision theory
 R (Computer program language)
 Bayesian statistical decision theory
 R (Computer program language)
 BayesEntscheidungstheorie
 Statistisches Modell
 R
 MATHEMATICS / Applied
 MATHEMATICS / Probability & Statistics / General
 Bayesian statistical decision theory
 R (Computer program language)
 BayesEntscheidungstheorie
 Statistisches Modell
 R
 MATHEMATICS / Applied
 MATHEMATICS / Probability & Statistics / General
 Label
 Statistical rethinking : a Bayesian course with examples in R and Stan, Richard McElreath, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
 Note
 "A CRC title."
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents
 The golem of Prague  Small worlds and large worlds  Sampling the imaginary  Linear models  Multivariate linear models  Overfitting, regularization, and information criteria  Interactions  Markov chain Monte Carlo  Big entropy and the generalized linear model  Counting and classification  Monsters and mixtures  Multilevel models  Adventures in covariance  Missing data and other opportunities  Horoscopes
 Control code
 956953600
 Dimensions
 unknown
 Extent
 1 online resource (xvii, 469 pages)
 File format
 unknown
 Form of item
 online
 Isbn
 9781482253467
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other physical details
 illustrations.
 http://library.link/vocab/ext/overdrive/overdriveId
 948947
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)956953600
 Label
 Statistical rethinking : a Bayesian course with examples in R and Stan, Richard McElreath, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
 Note
 "A CRC title."
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code

 txt
 Content type MARC source
 rdacontent
 Contents
 The golem of Prague  Small worlds and large worlds  Sampling the imaginary  Linear models  Multivariate linear models  Overfitting, regularization, and information criteria  Interactions  Markov chain Monte Carlo  Big entropy and the generalized linear model  Counting and classification  Monsters and mixtures  Multilevel models  Adventures in covariance  Missing data and other opportunities  Horoscopes
 Control code
 956953600
 Dimensions
 unknown
 Extent
 1 online resource (xvii, 469 pages)
 File format
 unknown
 Form of item
 online
 Isbn
 9781482253467
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other physical details
 illustrations.
 http://library.link/vocab/ext/overdrive/overdriveId
 948947
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)956953600
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
Genre
Member of
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.library.missouri.edu/portal/StatisticalrethinkingaBayesiancoursewith/SbLWpw2XFOM/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/StatisticalrethinkingaBayesiancoursewith/SbLWpw2XFOM/">Statistical rethinking : a Bayesian course with examples in R and Stan, Richard McElreath, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>