Composing inference algorithms as program transformations

Robert Zinkov, Chung-chieh Shan
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:191-200, 2017.

Abstract

Probabilistic inference procedures are usually coded painstakingly from scratch, for each tar- get model and each inference algorithm. We reduce this effort by generating inference pro- cedures from models automatically. We make this code generation modular by decomposing inference algorithms into reusable program-to- program transformations. These transforma- tions perform exact inference as well as gener- ate probabilistic programs that compute expec- tations, densities, and MCMC samples. The resulting inference procedures are about as ac- curate and fast as other probabilistic program- ming systems on real-world problems.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-zinkov17a, title = {Composing inference algorithms as program transformations}, author = {Zinkov, Robert and Shan, Chung-chieh}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {191--200}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/zinkov17a/zinkov17a.pdf}, url = {https://proceedings.mlr.press/r15/zinkov17a.html}, abstract = {Probabilistic inference procedures are usually coded painstakingly from scratch, for each tar- get model and each inference algorithm. We reduce this effort by generating inference pro- cedures from models automatically. We make this code generation modular by decomposing inference algorithms into reusable program-to- program transformations. These transforma- tions perform exact inference as well as gener- ate probabilistic programs that compute expec- tations, densities, and MCMC samples. The resulting inference procedures are about as ac- curate and fast as other probabilistic program- ming systems on real-world problems.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Composing inference algorithms as program transformations %A Robert Zinkov %A Chung-chieh Shan %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-zinkov17a %I PMLR %P 191--200 %U https://proceedings.mlr.press/r15/zinkov17a.html %V R15 %X Probabilistic inference procedures are usually coded painstakingly from scratch, for each tar- get model and each inference algorithm. We reduce this effort by generating inference pro- cedures from models automatically. We make this code generation modular by decomposing inference algorithms into reusable program-to- program transformations. These transforma- tions perform exact inference as well as gener- ate probabilistic programs that compute expec- tations, densities, and MCMC samples. The resulting inference procedures are about as ac- curate and fast as other probabilistic program- ming systems on real-world problems. %Z Reissued by PMLR on 04 October 2026.
APA
Zinkov, R. & Shan, C.. (2017). Composing inference algorithms as program transformations. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:191-200 Available from https://proceedings.mlr.press/r15/zinkov17a.html. Reissued by PMLR on 04 October 2026.

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