Doing Bayesian data analysis

• Verfasst von: Kruschke, John K. [VerfasserIn]
• Titel: Doing Bayesian data analysis
• Titelzusatz: a tutorial with R, JAGS, and Stan
• Verf.angabe: John K. Kruschke, Dept. of Psychological and Brain Sciences, Indiana University, Bloomington
• Ausgabe: Edition 2
• Verlagsort: Amsterdam ; Boston ; Heidelberg ; London ; New York ; Oxford ; Paris ; San Diego ; San Francisco ; Singapore ; Sydney ; Tokyo
• Verlag: Elsevier, Academic Press
• E-Jahr: 2015
• Jahr: [2015]
• Umfang: xii, 759 Seiten
• Illustrationen: Illustrationen, Diagramme
• Format: 25 cm
• Fussnoten: Hier auch später erschienene, unveränderte Nachrucke ; Author: Dr. John K. Kruschke (Dept. of Psychological and Brain Sciences, Indiana University, Bloomington) ; Literaturverzeichnis: Seite 737-745 und Index ; Auf dem Umschlag: Second Edition
• ISBN: 978-0-12-405888-0
• Schlagwörter: (s)Bayes-Entscheidungstheorie
• Schlagwörter: (s)Wissenschaftstheorie / (s)Wissenschaft / (s)Methode / (s)Lerntechnik / (s)Unterrichtsmethode / (s)Analyse / (s)Verfahren
• Dokumenttyp: Lehrbuch
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Online-Ausgabe: Doing Bayesian data analysis. - 2nd Edition. - Amsterdam [u.a.] : AP, Academic Press/Elsevier, 2015. - Online Ressource (v, 759 pages)
• Bibliogr. Hinweis: Erscheint auch als : Online-Ausgabe: Kruschke, John K.: Doing Bayesian data analysis. - Edition 2. - Amsterdam : Elsevier, Academic Press, 2015. - 1 Online-Ressource (xii, 759 pages)
• RVK-Notation: CM 4400
• RVK-Notation: ST 250
• RVK-Notation: SK 830
• RVK-Notation: QH 233
• RVK-Notation: ST 601
• K10plus-PPN: 78816645X

Abstract

Provides 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

Abstract

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