Doing Bayesian data analysis : a tutorial with R and BUGS / John K. Kruschke. [electronic resource]

Kruschke, John K.
1st edition
Burlington, MA : Academic Press, c2011.
1 online resource (xvii, 653 p. ) ill. ;
Bayesian statistical decision theory.
R (Computer program language).
Electronic books.
System Details:
text file
"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 a rustya 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." - Publisher's description.
This book's organization : read me first!
Introduction : models we believe in
What is this stuff called probability?
Bayes' rule
Inferring a binomial proportion via exact mathematical analysis
Inferring a binomial proportion via grid approximation
Inferring a binomial proportion via the Metropolis algorithm
Inferring two binomial proportions via Gibbs sampling
Bernoulli likelihood with hierarchical prior
Hierarchical modeling and model comparison
Null hypothesis significance testing
Bayesian approaches to testing a point ("null") hypothesis
Goals, power, and sample size
Overview of the generalized linear model
Metric predicted variable on a single group
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
Ordinal predicted variable
Contingency table analysis
Tools in the trunk.
Includes bibliographical references and index.
Bibliographic Level Mode of Issuance: Monograph
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