| 000 | 02466cam a2200325 i 4500 | ||
|---|---|---|---|
| 001 | 0000029440 | ||
| 005 | 20230921135924.0 | ||
| 008 | 140320s2015 maum b a000 0 eng | ||
| 010 | _a2014011293 | ||
| 019 |
_a884310314 _a900488537 |
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| 020 |
_a9780124058880 (hbk) : _c$80.00 |
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| 020 | _a0124058884 (hbk) | ||
| 042 | _ac=Program for Cooperative Cataloging | ||
| 092 |
_a519.542 _bK945 |
||
| 100 | 1 | _aKruschke, John K. | |
| 245 | 1 | 0 |
_aDoing Bayesian data analysis : _ba tutorial with R, JAGS, and stan / _cJohn Kruschke. |
| 250 | _asecond [edition] | ||
| 260 |
_aBoston : _bAcademic Press, _c2015. |
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| 264 |
_aBoston : _bAcademic Press, _c2015 |
||
| 300 |
_axii, 759 pages : _billustrations ; _c25 cm. |
||
| 504 | _aIncludes bibliographical references (p.737-745) | ||
| 505 | 0 | _aWhat'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. | |
| 520 | _aProvides 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. | ||
| 650 | 0 | _aBayesian statistical decision theory. | |
| 650 | 0 | _aR (Computer program language) | |
| 908 | _a160803 | ||
| 913 | _aN | ||
| 989 | 7 | _a20230822095114.0 | |
| 999 |
_c13584 _d13584 |
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