Doing Bayesian Data Analysis A Tutorial with R JAGS and Stan 2 John Kruschke PDF VOX

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Doing Bayesian Data Analysis A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets.

The book is divided into three parts and begins with the basics models, probability, Bayes’ rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment.

This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business.

• Accessible, including the basics of essential concepts of probability and random sampling
• Examples with R programming language and JAGS software
• Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis)
• Coverage of experiment planning
• R and JAGS computer programming code on website
• Exercises have explicit purposes and guidelines for accomplishment
• Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs

ebook,John Kruschke,Doing Bayesian Data Analysis A Tutorial with R, JAGS, and Stan,Academic Press,Probability Statistics - Bayesian Analysis,Applied mathematics,BAYESIAN STATISTICS,Bayesian statistical decision theory,Bayesian statistical decision theory.,Bayesian statistics; Bayesian data analysis; Markov chain Monte Carlo; hierarchical models; generalized linear model; null hypothesis significance test,Computer Applications,General,MATHEMATICS,MATHEMATICS / Mathematical Analysis,MATHEMATICS / Probability Statistics / Bayesian Analysis,MATHEMATICS / Probability Statistics / General,Mathematical Analysis,Mathematics Statistics Textbooks,Mathematics/Mathematical Analysis,Non-Fiction,Physical Sciences,Probability Statistics,Probability Statistics - Bayesian Analysis,Probability Statistics - General,R (Computer program language),R (Computer program language).,Scholarly/Undergraduate,Science / Mathematics,Science/Math,Science/Mathematics,TEXT,Textbooks (Various Levels),Western Europe,MATHEMATICS / Mathematical Analysis,MATHEMATICS / Probability Statistics / Bayesian Analysis,MATHEMATICS / Probability Statistics / General,Mathematics/Mathematical Analysis,Probability Statistics - General,Mathematics,Bayesian Statistics,Science/Mathematics,Applied mathematics,Probability statistics

Doing Bayesian Data Analysis A Tutorial with R JAGS and Stan 2 John Kruschke Reviews :

Doing Bayesian Data Analysis A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets.

The book is divided into three parts and begins with the basics models, probability, Bayes’ rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment.

This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business.

• Accessible, including the basics of essential concepts of probability and random sampling
• Examples with R programming language and JAGS software
• Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis)
• Coverage of experiment planning
• R and JAGS computer programming code on website
• Exercises have explicit purposes and guidelines for accomplishment
• Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs

ebook,John Kruschke,Doing Bayesian Data Analysis A Tutorial with R, JAGS, and Stan,Academic Press,Probability Statistics - Bayesian Analysis,Applied mathematics,BAYESIAN STATISTICS,Bayesian statistical decision theory,Bayesian statistical decision theory.,Bayesian statistics; Bayesian data analysis; Markov chain Monte Carlo; hierarchical models; generalized linear model; null hypothesis significance test,Computer Applications,General,MATHEMATICS,MATHEMATICS / Mathematical Analysis,MATHEMATICS / Probability Statistics / Bayesian Analysis,MATHEMATICS / Probability Statistics / General,Mathematical Analysis,Mathematics Statistics Textbooks,Mathematics/Mathematical Analysis,Non-Fiction,Physical Sciences,Probability Statistics,Probability Statistics - Bayesian Analysis,Probability Statistics - General,R (Computer program language),R (Computer program language).,Scholarly/Undergraduate,Science / Mathematics,Science/Math,Science/Mathematics,TEXT,Textbooks (Various Levels),Western Europe,MATHEMATICS / Mathematical Analysis,MATHEMATICS / Probability Statistics / Bayesian Analysis,MATHEMATICS / Probability Statistics / General,Mathematics/Mathematical Analysis,Probability Statistics - General,Mathematics,Bayesian Statistics,Science/Mathematics,Applied mathematics,Probability statistics

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Product details File Size 27331 KB Print Length 746 pages Publisher Academic Press; 2 edition (November 11, 2014) Publication Date November 11, 2014 Sold by  Services LLC Language English ASIN B00PYZ2VR6

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