Doing Bayesian Data Analysis: A Tutorial with R and BUGS
- 12h 16m
- John K. Kruschke
- Elsevier Science and Technology Books, Inc.
- 2011
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.
- Provides complete examples with R programming language and BUGS software (both Freeware)
- Addresses topics such as experiment planning, power analysis and sample size planning
- Includes numerous exercises with explicit purposes and guidelines for accomplishment.
About the Author
John K. Kruschke has taught Bayesian data analysis, mathematical modeling, and traditional statistical methods for over 20 years. He is seven-time winner of Teaching Excellence Recognition Awards from Indiana University, where he is Professor of Psychological and Brain Sciences, and Adjunct Professor of Statistics. He has presented numerous talks and workshops on Bayesian data analysis for specially convened groups and professional conferences. He received a Troland Research Award from the National Academy of Sciences. He is an Action Editor for the Journal of Mathematical Psychology, and is or has been on the editorial boards of several other journals, including Psychological Review and Psychonomic Bulletin & Review.
In this Book
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This Book's Organization—Read Me First!
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Introduction—Models We Believe in
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What is This Stuff Called Probability?
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Bayes' Rule
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Inferring a Binomial Proportion via Exact Mathematical Analysis
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Inferring a Binomial Proportion via Grid Approximation
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Inferring a Binomial Proportion via the Metropolis Algorithm
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Inferring Two Binomial Proportions via Gibbs Sampling
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Bernoulli Likelihood with Hierarchical Prior
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Hierarchical Modeling and Model Comparison
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Null Hypothesis Significance Testing
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Bayesian Approaches to Testing a Point (“Null”)Hypothesis
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Goals, Power, and Sample Size
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Overview of the Generalized Linear Model
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Metric Predicted Variable on a Single Group
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Metric Predicted Variable with One Metric Predictor
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Metric Predicted Variable with Multiple Metric Predictors
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Metric Predicted Variable with One Nominal Predictor
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Metric Predicted Variable with Multiple Nominal Predictors
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Dichotomous Predicted Variable
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Ordinal Predicted Variable
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Contingency Table Analysis
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Tools in the Trunk
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References
