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

- Edition:
- 1st edition
- Publication:
- Burlington, MA : Academic Press, c2011.
- Format/Description:
- Book

1 online resource (xvii, 653 p. ) ill. ; - Subjects:
- Bayesian statistical decision theory.

R (Computer program language). - Form/Genre:
- Electronic books.
- Language:
- English
- System Details:
- text file
- Summary:
- "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.
- Contents:
- 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. - Notes:
- Includes bibliographical references and index.

Bibliographic Level Mode of Issuance: Monograph - ISBN:
- 1-282-95496-2

9786612954962

0-12-381486-3 -
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