The free online version of the book is available here.
You can access the source code for the book from here.
You can buy a physical copy of the book too.
Why you might want to follow this course of lectures
Here
is a talk I gave in Indiana University in September 2022. This talk
explains why I believe that researchers in linguistics and psychology need to
spend a little bit of time studying the statistical tools they need for their research.
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 (mc-stan.org).
However, the underlying theory needed to
use this software 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 book and the accompanying 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 (mixed) models 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.
MOOC on OpenHPI
You can do a self-paced version of this course (chapters 1-4),
with auto-graded exercises, here.
Some 5000 people have signed up for this course.
Is this course taught in person?
We regularly teach parts of this course in person at:
The annual Statistical Methods for Linguistics and Psychology (SMLP), Potsdam, Germany. This is an annual week-long summer school, held usually just before the AMLaP conference. Chapters 1-5 and 13 are covered in Intro Bayes, and the rest of the chapters in the Advanced Bayes track.
The University of Potsdam, Germany. This is a two-semester course that covers the entire book (except the last chapter).
The Indian Institute of Technology, Kanpur. Himanshu Yadav, also teaches this material, in India.
Prerequisites
You must have a functioning computer to do this course.
We also assume familiarity with R. Participants will benefit most if they have
previously fit linear models and linear mixed models (using lme4) in R, in any
scientific domain within linguistics and psychology. No knowledge of calculus or
linear algebra is assumed (but will be helpful to know), but basic school level
mathematics knowledge is assumed.
Finally, to follow this course, it is important to be familiar with the basics of
R Markdown.
Please read the instructions regarding homework submission (if you are submitting homework as part of a summer school course).
Please install the following software before watching the videos
We will be using the software R, and RStudio or Visual Studio Code, so make sure you install these on
your computer. You should also install the R package rstan;
the R package brms, and all the packages mentioned in the introduction to the book.
Please follow the installation instructions carefully.
Install the library bcogsci from here.
Outcomes
After completing this course, the participant will have become familiar with the
foundations of Bayesian inference using brms, and will be able to fit a range of
multiple regression models and hierarchical models, for normally distributed
data, and for lognormal and binomially distributed data. They will know how to
calibrate their models using prior and posterior predictive checks.
Some example articles that use Bayesian methods from our lab
A frequently asked question is: how to summarize the results of a Bayesian
analysis? Here are some examples of articles we have published using Bayesian
data analysis. Our presentation of results is continuously evolving; there is no
fixed answer to the question, how should I display my results? Use your
judgement. The most important thing you can do to facilitate understanding of
your work is to be open and transparent about your analyses. This means
releasing all data and code with the published paper (see the reproducible workflows lecture above).
For other papers that use Bayesian methods, see my post-2012 publications.