# Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists

arXiv: https://arxiv.org/abs/1506.06201 · HTML: https://mlsanity.com/paper/1506.06201/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2015.

## Built on by (most cited first, 3 of 3)

- [Bayesian Statistical Modeling with Predictors from LLMs](https://mlsanity.com/paper/2406.09012/) (2024)
- [SEAM: An Integrated Activation-Coupled Model of Sentence Processing and Eye Movements in Reading](https://mlsanity.com/paper/2303.05221/) (2023)
- [AI-Mediated Communication Can Steer Collective Opinion](https://mlsanity.com/paper/2605.16245/) (2026)

## Abstract (arXiv)

With the arrival of the R packages nlme and lme4, linear mixed models (LMMs) have come to be widely used in experimentally-driven areas like psychology, linguistics, and cognitive science. This tutorial provides a practical introduction to fitting LMMs in a Bayesian framework using the probabilistic programming language Stan. We choose Stan (rather than WinBUGS or JAGS) because it provides an elegant and scalable framework for fitting models in most of the standard applications of LMMs. We ease the reader into fitting increasingly complex LMMs, first using a two-condition repeated measures self-paced reading study, followed by a more complex 2× 2 repeated measures factorial design that can be generalized to much more complex designs.