  Deriving conditional and marginal distributions using conjugacy relationships
can be time consuming and error prone. In this paper, 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.
