Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan / John K. Kruschke.
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TextPublication details: Boston : Academic Press, ©2015.Edition: 2nd edDescription: xii, 759 p. : illustrations ; 25 cmISBN: - 9780124058880 (hbk)
- 000SA.161 23 K94
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.161 K94 (Browse shelf(Opens below)) | Available | 137014 |
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| 000SA.161 H289 Bayesian inference : | 000SA.161 In61 Bayesian statistics 4 : | 000SA.161 J48 Bayesian inference in the social sciences / | 000SA.161 K94 Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan / | 000SA.161 L479 Bayesian statistics : | 000SA.161 L481 Structural equation modeling | 000SA.161 L744 Bayesian probability theory : |
Includes bibliographical references and index.
1. What's in this book (Read this first!) --
Part I The basics: models, probability, Bayes' rule and r:
2. Introduction: credibility, models, and parameters;
3. The R programming language;
4. What is this stuff called probability?;
5. Bayes' rule --
Part II All the fundamentals applied to inferring a binomila probability:
6. Inferring a binomial probability via exact mathematical analysis;
7. Markov chain Monte Carlo; JAGS;
8. JAGS;
9. Hierarchical models;
10. Model comparison and hierarchical modeling;
11. Null hypothesis significance testing;
12. Bayesian approaches to testing a point ("Null") hypothesis; 13. Goals, power, and sample size; Stan --
Part III The generalized linear model:
14. Stan;
15. Overview of the generalized linear model;
16. Metric-predicted variable on one or two groups;
17. Metric predicted variable with one metric predictor;
18. Metric predicted variable with multiple metric predictors;
19. Metric predicted variable with one nominal predictor;
20. Metric predicted variable with multiple nominal predictors; 21. Dichotomous predicted variable;
22. Nominal predicted variable;
23. Ordinal predicted variable;
24. Count predicted variable;
25. Tools in the trunk --
Bibliography --
Index.
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.
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