Santiago Barreda, Noah Silbert

Bayesian Multilevel Models for Repeated Measures Data

A Conceptual and Practical Introduction in R

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 460
• Erscheinungstermin: 18. Mai 2023
• Englisch
• Abmessung: 244mm x 170mm x 27mm
• Gewicht: 1080g
• ISBN-13: 9781032259628
• ISBN-10: 1032259620
• Artikelnr.: 67402089

Autorenporträt

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