Papers › Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language

Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language

29 Nov 2018NeurIPS 2018 12arXiv:1811.11926archive 2025-07-28

Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran

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.

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categorical_gen_log_prob google-research/autoconj/autoconj/log_probs.py official repository unverified Apache-2.0 (permissive) · c705fdeedd76a185 · report
cavi google-research/autoconj/autoconj/meanfield.py official repository unverified Apache-2.0 (permissive) · da702c67e99ec146 · report
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find_distribution google-research/autoconj/autoconj/exponential_families.py official repository unverified Apache-2.0 (permissive) · bca42d1ba8b67d66 · report
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make_log_joint_fn google-research/autoconj/autoconj/pplham.py official repository unverified Apache-2.0 (permissive) · 611a2db65fa8e846 · report
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