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Doing Bayesian Data Analysis

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Engelsk
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 ex...

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Forlagsbeskrivelse af Doing Bayesian Data Analysis af John Kruschke

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.

Detaljer

Forlag
Academic Press Inc
ISBN
9780124058880
Sprog
Engelsk
Sider
776
Udgivelsesdato
30-12-2014
Format
Hardback
Varenr.
3334985
EAN nr.
9780124058880
Varegruppe
Engelske textbooks
Højde/Dybde (mm)
236
Bredde (mm)
199
Længde (mm)
38
Vægt (g)
1784

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  • Matematik og naturvidenskabMatematikSandsynlighedsregning og statistik
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