This book introduces Bayesian data analysis and Bayesian cognitive modeling to students and researchers in cognitive science (e.g. linguistics, psycholinguistics, psychology, computer science) with a focus on modeling data from planned experiments. The book relies on the probabilistic programming language Stan and the R package brms.
This book introduces Bayesian data analysis and Bayesian cognitive modeling to students and researchers in cognitive science (e.g. linguistics, psycholinguistics, psychology, computer science) with a focus on modeling data from planned experiments. The book relies on the probabilistic programming language Stan and the R package brms.
Bruno Nicenboim is an assistant professor in the department of Cognitive Science and Artificial Intelligence at Tilburg University in the Netherlands, working within the area of computational psycholinguistics. Daniel J. Schad is a cognitive psychologist and is professor of Quantitative Methods at the HMU Health and Medical University in Potsdam, Germany. Shravan Vasishth is professor of psycholinguistics at the department of Linguistics at the University of Potsdam, Germany; he is a chartered statistician (Royal Statistical Society, UK).
Inhaltsangabe
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
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
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