Following on from last week’s post on Principled Bayesian Workflow I want to reflect on how to motivate a model. The purpose of most models is to understand change, and yet, considering what doesn’t change and should be kept constant can be equally important. I will go through a couple of models in this post to illustrate this idea. The purpose of the model I want to build today is to predict how much ice cream is sold for different temperatures $$(t)$$.