Doing Bayesian data analysis [electronic resource] : a tutorial with R and BUGS / John K. Kruschke.
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Amsterdam ; Boston :
Academic Press,
c2011.
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Formato: | Electrónico eBook |
Table of 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.