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 Model-based Statistics, The Book Pushes Readers To Perform Step-by-step 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 Resource The 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.

lgli/Z:\Bibliotik_\A Library\2015 Richard McElreath - Statistical Rethinking - A Bayesian Course with Examples in R and Stan_Rebsl.pdf

lgrsnf/Z:\Bibliotik_\A Library\2015 Richard McElreath - Statistical Rethinking - A Bayesian Course with Examples in R and Stan_Rebsl.pdf

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zlib/no-category/McElreath, Richard/Statistical rethinking: a Bayesian course with examples in R and Stan_10666627.pdf

Richard McElreath

Chapman and Hall/CRC

CRC Press LLC

Chapman & Hall/CRC textbooks in computing, Boca Raton, Fl, 2016

Texts in statistical science, Boca Raton, 2016

United States, United States of America

CRC Press LLC, Boca Raton, 2016

2018

lg2855273

producers:
XeTeX 0.99992

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Front Cover -- Contents -- Preface -- 1: The Golem of Prague -- 2: Small Worlds and Large Worlds -- 3: Sampling the Imaginary -- 4: Linear Models -- 5: Multivariate Linear Models -- 6: Overfitting, Regularization, and Information Criteria -- 7: Interactions -- 8: Markov Chain Monte Carlo -- 9: Big Entropy and the Generalized Linear Model -- 10: Counting and Classification -- 11: Monsters and Mixtures -- 12: Multilevel Models -- 13: Adventures in Covariance -- 14: Missing Data and Other Opportunities -- 15: Horoscopes -- Endnotes -- Bibliography -- Back Cover

2020-11-29