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 This is code for the NeurIPS 2018 paper ["Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language"](https://papers.nips.cc/paper/8270-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.
+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.