Doing Bayesian Data Analysis - Info and Reading Options
A Tutorial Introduction with R
By John K. Kruschke
"Doing Bayesian Data Analysis" was published by Elsevier Science & Technology Books in 2010 - Amsterdam, it has 25311 pages and the language of the book is English.
“Doing Bayesian Data Analysis” Metadata:
- Title: Doing Bayesian Data Analysis
- Author: John K. Kruschke
- Language: English
- Number of Pages: 25311
- Publisher: ➤ Elsevier Science & Technology Books
- Publish Date: 2010
- Publish Location: Amsterdam
“Doing Bayesian Data Analysis” Subjects and Themes:
- Subjects: ➤ Bayesian statistical decision theory - R (Computer program language) - Programming languages (electronic computers) - Bayes-Verfahren - R. - General - Mathematics & statistics -> mathematics -> mathematics general - Mathematics & statistics -> mathematics -> probability - r
Edition Specifications:
- Pagination: 672
Edition Identifiers:
- The Open Library ID: OL35767024M - OL16799762W
- ISBN-13: 9780123814869 - 9780123814852
- All ISBNs: 9780123814869 - 9780123814852
AI-generated Review of “Doing Bayesian Data Analysis”:
"Doing Bayesian Data Analysis" Description:
Open Data:
There 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 BUGSprovides 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, 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.Author website: http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/ -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
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