[貝葉斯專著]Doing Bayesian Data Analysis: A Tutorial with R and BUGS

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Doing Bayesian Data Analysis: A Tutorial with R and BUGS

John Kruschke

Book Description
Publication Date: Oct. 27 2010 |
ISBN-10: 0123814855 | ISBN-13: 978-0123814852 |
Edition: 1
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and 'rusty' calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods.
-Accessible, including the basics of essential concepts of probability and random sampling
-Examples with R programming language and BUGS 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 BUGS computer programming code on website
-Exercises have explicit purposes and guidelines for accomplishment
Product Details

Hardcover: 672 pages
Publisher: Academic Press; 1 edition (Oct. 27 2010)
Language: English
ISBN-10: 0123814855
ISBN-13: 978-0123814852
Product Dimensions: 23.6 x 19.3 x 3.3 cm
Shipping Weight: 1.2 Kg

Product DescriptionReview"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

From the Back CoverThere is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, 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. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, correlation, multiple regression, and chi-square (contingency table analysis).
This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus.