Book (in progress): An Introduction to Bayesian Data Analysis for Cognitive Science, CRC Press
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Motivation for writing this book
In recent years, Bayesian methods have come to be widely adopted in all areas of science. This is in large part due to the development of sophisticated software for probabilisic programming; a recent example is the astonishing computing capability afforded by the language, Stan
However, the underlying theory needed to use such computational tools sensibly is often inaccessible because end-users don't necessarily have the statistical and mathematical background to read the primary textbooks (such as Gelman et al's classic Bayesian Data Analysis, 3rd edition). In this course, we seek to cover this gap, by providing a relatively accessible and technically non-demanding introduction to the basic workflow for fitting different kinds of linear and non-linear models (e.g., linear mixed models, distributional regression models, mixture models, multinomial processing trees, etc.) using Stan. To illustrate the capability of Bayesian modeling, we will use the R package RStan and a powerful front-end R package for Stan called brms.
The book is aimed at researchers working in experimentally-focused areas (psychology, linguistics, psycholinguistics, and related disciplines). For this reason, we use real data-sets from psycholinguistics and related fields to demonstrate the capabilities of Bayesian modeling.