Part I: The Basics: Models, Probability, Bayes’ Rule, and R
by John Kruschke
Doing Bayesian Data Analysis, 2nd Edition
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Chapter 1: What's in This Book (Read This First!)
- Part I: The Basics: Models, Probability, Bayes’ Rule, and R
- Part II: All the Fundamentals Applied to Inferring a Binomial Probability
- Introduction
- Chapter 6: Inferring a Binomial Probability via Exact Mathematical Analysis
- Chapter 7: Markov Chain Monte Carlo
- Chapter 8: JAGS
- 8.1 Jags and its relation to R
- 8.2 A complete example
- 8.3 Simplified scripts for frequently used analyses
- 8.4 Example: difference of biases
- 8.5 Sampling from the prior distribution in jags
- 8.6 Probability distributions available in jags
- 8.7 Faster sampling with parallel processing in runjags
- 8.8 Tips for expanding jags models
- 8.9 Exercises
- Chapter 9: Hierarchical Models
- Chapter 10: Model Comparison and Hierarchical Modeling
- Chapter 11: Null Hypothesis Significance Testing
- Chapter 12: Bayesian Approaches to Testing a Point (“Null”) Hypothesis
- Chapter 13: Goals, Power, and Sample Size
- Chapter 14: Stan
- Part III: The Generalized Linear Model
- Introduction
- Chapter 15: Overview of the Generalized Linear Model
- Chapter 16: Metric-Predicted Variable on One or Two Groups
- Chapter 17: Metric Predicted Variable with One Metric Predictor
- Chapter 18: Metric Predicted Variable with Multiple Metric Predictors
- Chapter 19: Metric Predicted Variable with One Nominal Predictor
- Chapter 20: Metric Predicted Variable with Multiple Nominal Predictors
- 20.1 Describing groups of metric data with multiple nominal predictors
- 20.2 Hierarchical bayesian approach
- 20.3 Rescaling can change interactions, homogeneity, and normality
- 20.4 Heterogeneous variances and robustness against outliers
- 20.5 Within-subject designs
- 20.6 Model comparison approach
- 20.7 Exercises
- Chapter 21: Dichotomous Predicted Variable
- Chapter 22: Nominal Predicted Variable
- Chapter 23: Ordinal Predicted Variable
- Chapter 24: Count Predicted Variable
- Chapter 25: Tools in the Trunk
- Bibliography
Part I
The Basics: Models, Probability, Bayes’ Rule, and R
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