This is code for the NeurIPS 2018 paper "Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language" by Matthew D Hoffman*, Matthew J Johnson*, and Dustin Tran.

Deriving conditional and marginal distributions using conjugacy relationships can be time consuming and error prone. In this project, we propose a strategy for automating such derivations. Unlike previous systems which focus on relationships between pairs of random variables, our system (which we call AutoConj) operates directly on Python functions that compute log-joint distribution functions. Autoconj provides support for conjugacy-exploiting algorithms in any Python-embedded PPL. This paves the way for accelerating development of novel inference algorithms and structure-exploiting modeling strategies.

This is not an official Google product.