# Examples of applications of Markov random fields to data with a small number of variables

I am learning about some of the common applications of Markov random fields (a.k.a. undirected graphical models) to data science. A common feature of many applications I have read about is that the number of variables in the model is relatively large (e.g. in applications to computer vision or NLP). For educational purposes, I am interested in the following question:

Are there nice examples of MRF models where the number of variables in the model is very small (i.e. $$<10$$)?

Ideally, such examples would have some practical use. I would like more than just a toy model graph labelled with some variables (a real model based on a real data set is what I'm looking for).