We argue that probabilistic programming with undirected models, in order to scale up, needs to open up. That is, instead of focusing on minimal sets of generic building blocks such as universal quantification or logical connectives, languages should grow to include specific building blocks for as many uses cases as necessary. This can not only lead to more concise models, but also to more efficient inference if we use methods that can exploit building-block specific subroutines. As embodiment of this paradigm we present FAST FROWARD, a platform for implementing probabilistic programming languages that grow.
['Sebastian Riedel']
Declarative probabilistic programming for undirected graphical models: open up to scale up