| Edition |
2nd edition |
| Description |
1 online resource (v, 759 pages) : illustrations |
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text txt rdacontent |
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still image sti rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Note |
Previous edition: 2011. |
| Bibliography |
Includes bibliographical references (pages 737-745) and index. |
| Note |
Print version record. |
| Contents |
What's in this book (Read this first!) -- Part I The basics: models, probability, Bayes' rule and r: Introduction: credibility, models, and parameters; The R programming language; What is this stuff called probability?; Bayes' rule -- Part II All the fundamentals applied to inferring a binomila probability: Inferring a binomial probability via exact mathematical analysis; Markov chain Monte Carlo; JAGS; Hierarchical models; Model comparison and hierarchical modeling; Null hypothesis significance testing; Bayesian approaches to testing a point ("Null") hypothesis; Goals, power, and sample size; Stan -- Part III The generalized linear model: Overview of the generalized linear model; 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; Metric predicted variable with multiple nominal predictors; Dichotomous predicted variable; Nominal predicted variable; Ordinal predicted variable; Count predicted variable; Tools in the trunk -- Bibliography -- Index. |
| Summary |
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.--Provided by publisher. |
| Subject |
Bayesian statistical decision theory.
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R (Computer program language)
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Théorie de la décision bayésienne.
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R (Langage de programmation)
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Bayesian statistical decision theory
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R (Computer program language)
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Bayes-Entscheidungstheorie
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R Programm
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| Other Form: |
Print version: Kruschke, John K. Doing Bayesian data analysis. Edition 2. London, UK ; San Diego, CA : Academic Press, [2015] 9780124058880 (DLC) 2014011293 (OCoLC)897342420 |
| ISBN |
9780124059160 |
|
0124059163 |
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9780124058880 |
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0124058884 |
| Standard No. |
AU@ 000060991764 |
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CHNEW 001012569 |
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CHNEW 001026537 |
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CHVBK 519302338 |
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DEBSZ 434138177 |
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GBVCP 821857584 |
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NLGGC 391541269 |
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UKMGB 017990610 |
|
