Bayesian Multilevel Models for Repeated Measures Data

Preface<\/p>

1. Introduction: Experiments and Variables<\/p>

2. Probabilities, Likelihood, and Inference<\/p>

3. Fitting Bayesian Regression Models with brms<\/i><\/p>

4. Inspecting a `Single Group\u00bf of Observations using a Bayesian Multilevel Model<\/p>

5. Comparing Two Groups of Observations: Factors and Contrasts<\/p>

6. Variation in Parameters (`Random Effects\u00bf) and Model Comparison<\/p>

7. Comparing Many Groups, Interactions, and Posterior Predictive Checks<\/p>

8. Varying Variances, More about Priors, and Prior Predictive Checks<\/p>

9. Quantitative Predictors and their Interactions with Factors<\/p>

10. Logistic Regression and Signal Detection Theory Models<\/p>

11. Multiple Quantitative Predictors, Dealing with Large Models, and Bayesian ANOVA<\/p>

12. Multinomial and Ordinal Regression<\/p>

13. Writing up Experiments: An investigation of the Perception of Apparent Speaker Characteristics from Speech Acoustics<\/p>"