Introduction to Bayesian Data Analysis for Cognitive Science - Nicenboim, Bruno

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Résumé :
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....

Biographie:
the main insight here is that the posterior distribution of a parameter is a compromise between the prior and the likelihood functions. The book then gradually builds up the regression framework using the brms package in R, ultimately leading to hierarchical regression modeling (aka the linear mixed model). Along the way, there is detailed discussion about the topic of prior selection, and developing a well-defined workflow. Later chapters introduce the Stan programming language, and cover advanced topics using practical examples: contrast coding, model comparison using Bayes factors and cross-validation, hierarchical models and reparameterization, defining custom distributions, measurement error models and meta-analysis, and finally, some examples of cognitive models: multinomial processing trees, finite mixture models, and accumulator models. Additional chapters, appendices, and exercises are provided as online materials and can be accessed here: https://github.com/bnicenboim/bayescogsci.