Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Machine Learning
2018-11-30 v1 Programming Languages
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1811.11926,
title = {Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language},
author = {Matthew D. Hoffman and Matthew J. Johnson and Dustin Tran},
journal= {arXiv preprint arXiv:1811.11926},
year = {2018}
}
Comments
Appears in Neural Information Processing Systems, 2018. Code available at https://github.com/google-research/autoconj