This work proposes Mixed-Effect Bayesian Network (MEBN) as a method for modeling the effects of nutrition. It allows identifying both typical and personal correlations between nutrients and their bodily responses. Predicting a personal network of nutritional reactions would allow interesting applications at personal diets and in understanding this complex system. Brief theory of MEBN is first given, followed by the implementation in R and Stan. A real life dataset from a nutritional study (Sysdimet) is then analyzed with this method and the results are visualized with a responsive JavaScript-visualization.

Code and data available at github.com/stan-dev/stancon_talks