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Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan by Kruschke, Jo
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eBay item number:234940468629
Item specifics
- Condition
- Book Title
- Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan
- ISBN
- 9780124058880
- Publication Year
- 2014
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Publication Name
- Doing Bayesian Data Analysis : a Tutorial with R, Jags, and Stan
- Item Length
- 9.2in
- Publisher
- Elsevier Science & Technology
- Item Width
- 7.5in
- Number of Pages
- 776 Pages
About this product
Product Information
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition 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. Accessible, including the basics of essential concepts of probability and random sampling Examples with R programming language and JAGS software Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis) Coverage of experiment planning R and JAGS computer programming code on website Exercises have explicit purposes and guidelines for accomplishment Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs
Product Identifiers
Publisher
Elsevier Science & Technology
ISBN-10
0124058884
ISBN-13
9780124058880
eBay Product ID (ePID)
202494256
Product Key Features
Publication Name
Doing Bayesian Data Analysis : a Tutorial with R, Jags, and Stan
Format
Hardcover
Language
English
Publication Year
2014
Type
Textbook
Number of Pages
776 Pages
Dimensions
Item Length
9.2in
Item Width
7.5in
Additional Product Features
Lc Classification Number
Qa279.5.K79 2014
Edition Number
2
Reviews
"I think it fills a gaping hole in what is currently available, and will serve to create its own market as researchers and their students transition towards the routine application of Bayesian statistical methods.' -Prof. Michael lee, University of California, Irvine, and president of the Society for Mathematical Psychology "Kruschke's text covers a much broader range of traditional experimental designs...has the potential to change the way most cognitive scientists and experimental psychologists approach the planning and analysis of their experiments" -Prof. Geoffrey Iverson, University of California, Irvine, and past president of the Society for Mathematical Psychology "John Kruschke has written a book on Statistics. It's better than others for reasons stylistic. It also is better because itis Bayesian. To find out why, buy it -- it's truly amazin'!?-James L. (Jay) McClelland, Lucie Stern Professor & Chair, Dept. Of Psychology, Standford University, "I think it fills a gaping hole in what is currently available, and will serve to create its own market as researchers and their students transition towards the routine application of Bayesian statistical methods." -Prof. Michael lee, University of California, Irvine, and president of the Society for Mathematical Psychology "Kruschke's text covers a much broader range of traditional experimental designs...has the potential to change the way most cognitive scientists and experimental psychologists approach the planning and analysis of their experiments" -Prof. Geoffrey Iverson, University of California, Irvine, and past president of the Society for Mathematical Psychology "John Kruschke has written a book on Statistics. It's better than others for reasons stylistic. It also is better because itis Bayesian. To find out why, buy it -- it's truly amazin'!"-James L. (Jay) McClelland, Lucie Stern Professor & Chair, Dept. Of Psychology, Standford University, "I think it fills a gaping hole in what is currently available, and will serve to create its own market as researchers and their students transition towards the routine application of Bayesian statistical methods." -Prof. Michael lee, University of California, Irvine, and president of the Society for Mathematical Psychology "Kruschke's text covers a much broader range of traditional experimental designs.has the potential to change the way most cognitive scientists and experimental psychologists approach the planning and analysis of their experiments" -Prof. Geoffrey Iverson, University of California, Irvine, and past president of the Society for Mathematical Psychology "John Kruschke has written a book on Statistics. It's better than others for reasons stylistic. It also is better because itis Bayesian. To find out why, buy it -- it's truly amazin'!"-James L. (Jay) McClelland, Lucie Stern Professor & Chair, Dept. Of Psychology, Standford University, "Both textbook and practical guide, this work is an accessible account of Bayesian data analysis starting from the basics...This edition is truly an expanded work and includes all new programs in JAGS and Stan designed to be easier to use than the scripts of the first edition, including when running the programs on your own data sets." -- MAA Reviews "fills a gaping hole in what is currently available, and will serve to create its own market" --Prof. Michael Lee, U. of Cal., Irvine; pres. Society for Mathematical Psych "has the potential to change the way most cognitive scientists and experimental psychologists approach the planning and analysis of their experiments" --Prof. Geoffrey Iverson, U. of Cal., Irvine; past pres. Society for Mathematical Psych. "better than others for reasons stylistic.... buy it -- it's truly amazin'!" --James L. (Jay) McClelland, Lucie Stern Prof. & Chair, Dept. of Psych., Stanford U. "the best introductory textbook on Bayesian MCMC techniques" --J. of Mathematical Psych. "potential to change the methodological toolbox of a new generation of social scientists" --J. of Economic Psych. "revolutionary" --British J. of Mathematical and Statistical Psych. "writing for real people with real data. From the very first chapter, the engaging writing style will get readers excited about this topic" --PsycCritiques
Table of Content
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 Binomial Probability 6. Inferring a Binomial Probability via Exact Mathematical Analysis 7. Markov Chain Monte Carlo 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 14. Stan PART III The Generalized Linear Model 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
Copyright Date
2015
Topic
Programming Languages / General, Probability & Statistics / General, Probability & Statistics / Bayesian Analysis, Mathematical Analysis
Lccn
2014-011293
Dewey Decimal
519.5/42
Intended Audience
College Audience
Dewey Edition
23
Illustrated
Yes
Genre
Computers, Mathematics
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Most relevant reviews
- Jun 28, 2018
great explanation of Bayesian data analysis
Verified purchase: YesCondition: Pre-Owned
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