Doing Bayesian Data Analysis - A Tutorial with R, Jags, and Stan

Era-circus.be Doing Bayesian Data Analysis - A Tutorial with R, Jags, and Stan Image
INFORMATION
DATE DE PUBLICATION 2015-Jan-01
AUTEUR John K. Kruschke
ISBN 9780124058880
TAILLE DU FICHIER 7,30 MB
TÉLÉCHARGER
LIRE EN LIGNE
DESCRIPTION
Doing Bayesian Data Analysis : A Tutorial with R, JAGS, and Stan, Second Edition, offers step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R, JAGS, and Stan. This book presents a unique tutorial progression of chapters from introductory concepts and explanations of probability and Bayes' rule to advanced contemporary techniques and applications including Bayesian multiple regression, analysis of variance (ANOVA), logistic regression, contingency table analysis, model comparison, null hypothesis testing, hierarchical models, and Bayesian power analysis for planning of sample size. This book is richly illustrated and has numerous exercises with explicitly stated purposes to provide you with the additional hands-on guidance you need to build an understanding of concepts and apply them to mal data. The second edition has extensive new material including completely revised introductory chapters ; many new examples ; new chapters on R, JAGS, and Stan ; and all new programs. Key Features : Scaffolds from extensive introductory explanations for beginners to advanced applications for hands-on Bayesian data analysis Includes extensive coverage of topics afwell as power analysis and sample size planning to help you apply methods to your own data. Provides new, complete programs in R that are easily adaptable to your own applications and data Transforms complex content into highly relatable and easy-to-understand information with its conversational, light-hearted writing style.
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Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (English Edition) eBook: Kruschke, John: Amazon.es: Tienda Kindle

Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. Included are step-by-step ...

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