bnicenboim
commented
1 month ago all the code used is publicly available at https://bruno.nicenboim.me/bayescogsci/.
It sounds as if only the code is there. Can we change it to?
the content of the book including all the code used is publicly available at https://bruno.nicenboim.me/bayescogsci/.
here, maybe "within their own fields of cognitive science"?
Through this book, the reader will be able to develop a practical ability to apply Bayesian modeling within their own fields.
There is model comparison twice:
Later chapters introduce the Stan programming language, and covers advanced topics such as model comparison using Bayes factors and cross-validation for model comparison.
We could include all the advanced topics:
Later chapters introduce the Stan programming language, and covers advanced topics such as measurement error, meta-analysis, and model comparison using Bayes factors and cross-validation.
vasishth
I pushed the following text to the new repo:
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 particular focus on modeling data from planned experiments. The book relies on the probabilistic programming language Stan and the front-end R package brms. The main text in the book only assumes that the reader is familiar with the statistical programming language R, and has basic high school exposure to pre-calculus mathematics; the important mathematical constructs needed for the book are introduced in the first chapter, and all the code used is publicly available at https://bruno.nicenboim.me/bayescogsci/.
Through this book, the reader will be able to develop a practical ability to apply Bayesian modeling within their own fields. The book begins with an informal introduction to foundational topics such as probability theory, and univariate and bi-/multivariate discrete and continuous random variables. Then, the application of Bayes' rule for statistical inference is introduced with several simple analytical examples that require no computing software; 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 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 covers advanced topics such as model comparison using Bayes factors and cross-validation for model comparison. The last four chapters focus on implementing hierarchical computational cognitive models within the Stan environment; several practical examples from cognitive psychology and psycholinguistics are provided.
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 psychologists and is professor of Quantitative Methods at the 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).