Hello, world! Stan, PyMC3, and Edward - Statistical Modeling, Causal Inference, and Social Science

In both Stan and Edward, the program defining a model defines a joint log density that acts as a function from data sets to concrete posterior densities. In both Stan and Edward, the language distinguishes data variables from parameter values and provides an object-level representation of data variables. In PyMC3, the data is included as simple Python types in the model objects as the graph is built. So to get a model abstract, you'd have to write a function that takes the variables as arguments then returns the model instantiated with data. The definition of the deterministic node mu here is in terms of the actual data vectors X1 and X2--these aren't placeholders, their values are used from the containing environment.