BackCoverText.txt

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 R package brms, which is a front-end to Stan.
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; some of the important mathematical constructs needed
for the book are introduced in the first chapter. The R Markdown source code
that generated the book 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 field. 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 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.

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).