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Record Nr. |
UNINA9910465133103321 |
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Autore |
Kruschke John K. |
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Titolo |
Doing Bayesian data analysis : a tutorial with R, JAGS, and Stan / / John K. Kruschke |
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Pubbl/distr/stampa |
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Amsterdam : , : Academic Press is an imprint of Elsevier, , [2015] |
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©2015 |
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ISBN |
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0-12-405916-3 |
0-12-405888-4 |
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Edizione |
[Second edition.] |
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Descrizione fisica |
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1 online resource (xii, 759 pages ) : illustrations |
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Disciplina |
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Soggetti |
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Bayesian statistical decision theory |
R (Computer program language) |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references (pages 737-745). |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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Provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the |
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basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. |
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