What is this course? It is a crash course in brms: a brief and practical introduction to fitting Bayesian multilevel models in R and Stan using the brms package (Bayesian Regression Models using Stan). Because this is intended as a practical skills course, I will only give a quick and surface-level description of Bayesian analysis and how it can be used for multilevel regression models.
This course is designed for practicing researchers who have some experience with statistical analysis and coding in R.
If you don’t know what those packages are, don’t sweat it because you can just follow along by copying the code.
At the end of this course, you will understand …
At the end of this course, you will be able to …
This may or may not actually be a portrait of Thomas Bayes
In its simplest form, Bayesian inference is a method of statistical inference that allows you to use information you already know to assign a prior probability to a hypothesis, then update the probability of that hypothesis as you get more information. If that seems like common sense, it is!
It is a very powerful tool that has been applied across many fields of human endeavor. Today we are only going to look at its application in estimating the parameters of multilevel statistical models to analyze scientific data, but that’s only one of the many things we can use Bayesian inference for. Bayesian methods are only growing in popularity, thanks in large part to the rapid growth of user-friendly, open-source, computationally powerful software – like Stan and its companion R package brms that we are going to learn about today!
Bayesian inference is named after Reverend Thomas Bayes, an English clergyman, philosopher, and yes, statistician, who wrote a scholarly work on probability (published after his death in 1763). His essay included an algorithm to use evidence to find the distribution of the probability parameter of a binomial distribution … using what we now call Bayes’ Theorem! However, much of the foundations of Bayesian inference were really developed by Pierre-Simon Laplace independently of Bayes and at roughly the same time. Give credit where it’s due!