This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated-measures data, focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book.
This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated-measures data, focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book.
Santiago Barreda is a phonetician in the Linguistics Department at the University of California, Davis, USA, with a particular interest in speech perception. Noah Silbert is a former Academic and is currently a practicing Stoic. His training and background are in phonetics, perceptual modeling, and statistics.
Inhaltsangabe
Preface Acknowledgments 1. Introduction: Experiments and Variables 2. Probabilities, Likelihood, and Inference 3. Fitting Bayesian Regression Models with brms 4. Inspecting a 'Single Group' of Observations using a Bayesian Multilevel Model 5. Comparing Two Groups of Observations: Factors and Contrasts 6. Variation in Parameters ('Random Effects') and Model Comparison 7. Comparing Many Groups, Interactions, and Posterior Predictive Checks 8. Varying Variances, More about Priors, and Prior Predictive Checks 9. Quantitative Predictors and their Interactions with Factors 10. Logistic Regression and Signal Detection Theory Models 11. Multiple Quantitative Predictors, Dealing with Large Models, and Bayesian ANOVA 12. Multinomial and Ordinal Regression 13. Writing up Experiments: An investigation of the Perception of Apparent Speaker Characteristics from Speech Acoustics
Preface Acknowledgments 1. Introduction: Experiments and Variables 2. Probabilities, Likelihood, and Inference 3. Fitting Bayesian Regression Models with brms 4. Inspecting a 'Single Group' of Observations using a Bayesian Multilevel Model 5. Comparing Two Groups of Observations: Factors and Contrasts 6. Variation in Parameters ('Random Effects') and Model Comparison 7. Comparing Many Groups, Interactions, and Posterior Predictive Checks 8. Varying Variances, More about Priors, and Prior Predictive Checks 9. Quantitative Predictors and their Interactions with Factors 10. Logistic Regression and Signal Detection Theory Models 11. Multiple Quantitative Predictors, Dealing with Large Models, and Bayesian ANOVA 12. Multinomial and Ordinal Regression 13. Writing up Experiments: An investigation of the Perception of Apparent Speaker Characteristics from Speech Acoustics
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