Preface About the Authors I Foundational ideas 1 Introduction 2 Introduction to Bayesian data analysis II Regression models with brms 3 Computational Bayesian data analysis 4 Bayesian regression models 5 Bayesian hierarchical models 6 Contrast coding 7 Contrast coding with two predictor variables III Advanced models with Stan 8 Introduction to the probabilistic programming language Stan 9 Hierarchical models and reparameterization 10 Custom distributions in Stan IV Evidence synthesis and measurements with error 11 Meta-analysis and measurement error models V Model comparison 12 Introduction to model comparison 13 Bayes factors 14 Cross-validation VI Cognitive modeling with Stan 15 Introduction to cognitive modeling 16 Multinomial processing trees 17 Mixture models 18 A simple accumulator model to account for choice response time 19 In closing References