@inproceedings{towner2018incremental,
title                 = {Incremental inference for probabilistic programs},
author                = {Cusumano-Towner, Marco F. and Bichsel, Benjamin and Gehr, Timon and Vechev, Martin and Mansinghka, Vikash K.},
year                  = 2018,
booktitle             = {PLDI 2018: Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation},
pages                 = {571--585},
publisher             = {ACM},
url_paper             = {https://files.sri.inf.ethz.ch/website/papers/pldi18_interemental_inference_for_probabilistic_programs.pdf},
url_link              = {https://dl.acm.org/citation.cfm?id=3192399},
abstract              = {We present a novel approach for approximate sampling in probabilistic programs based on incremental inference. The key idea is to adapt the samples for a program $P$ into samples for a program $Q$, thereby avoiding the expensive sampling computation for program $Q$. To enable incremental inference in probabilistic programming, our work: (i) introduces the concept of a trace translator which adapts samples from $P$ into samples of $Q$, (ii) phrases this translation approach in the context of sequential Monte Carlo (SMC), which gives theoretical guarantees that the adapted samples converge to the distribution induced by $Q$, and (iii) shows how to obtain a concrete trace translator by establishing a correspondence between the random choices of the two probabilistic programs. We implemented our approach in two different probabilistic programming systems and showed that, compared to methods that sample the program $Q$ from scratch, incremental inference can lead to orders of magnitude increase in efficiency, depending on how closely related $P$ and $Q$ are.},
}

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K."],"year":2018,"bibtype":"inproceedings","biburl":"probcomp.csail.mit.edu/papers.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Incremental inference for probabilistic programs","author":[{"propositions":[],"lastnames":["Cusumano-Towner"],"firstnames":["Marco","F."],"suffixes":[]},{"propositions":[],"lastnames":["Bichsel"],"firstnames":["Benjamin"],"suffixes":[]},{"propositions":[],"lastnames":["Gehr"],"firstnames":["Timon"],"suffixes":[]},{"propositions":[],"lastnames":["Vechev"],"firstnames":["Martin"],"suffixes":[]},{"propositions":[],"lastnames":["Mansinghka"],"firstnames":["Vikash","K."],"suffixes":[]}],"year":"2018","booktitle":"PLDI 2018: Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation","pages":"571–585","publisher":"ACM","url_paper":"https://files.sri.inf.ethz.ch/website/papers/pldi18_interemental_inference_for_probabilistic_programs.pdf","url_link":"https://dl.acm.org/citation.cfm?id=3192399","abstract":"We present a novel approach for approximate sampling in probabilistic programs based on incremental inference. The key idea is to adapt the samples for a program $P$ into samples for a program $Q$, thereby avoiding the expensive sampling computation for program $Q$. To enable incremental inference in probabilistic programming, our work: (i) introduces the concept of a trace translator which adapts samples from $P$ into samples of $Q$, (ii) phrases this translation approach in the context of sequential Monte Carlo (SMC), which gives theoretical guarantees that the adapted samples converge to the distribution induced by $Q$, and (iii) shows how to obtain a concrete trace translator by establishing a correspondence between the random choices of the two probabilistic programs. We implemented our approach in two different probabilistic programming systems and showed that, compared to methods that sample the program $Q$ from scratch, incremental inference can lead to orders of magnitude increase in efficiency, depending on how closely related $P$ and $Q$ are.","bibtex":"@inproceedings{towner2018incremental,\ntitle = {Incremental inference for probabilistic programs},\nauthor = {Cusumano-Towner, Marco F. and Bichsel, Benjamin and Gehr, Timon and Vechev, Martin and Mansinghka, Vikash K.},\nyear = 2018,\nbooktitle = {PLDI 2018: Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation},\npages = {571--585},\npublisher = {ACM},\nurl_paper = {https://files.sri.inf.ethz.ch/website/papers/pldi18_interemental_inference_for_probabilistic_programs.pdf},\nurl_link = {https://dl.acm.org/citation.cfm?id=3192399},\nabstract = {We present a novel approach for approximate sampling in probabilistic programs based on incremental inference. The key idea is to adapt the samples for a program $P$ into samples for a program $Q$, thereby avoiding the expensive sampling computation for program $Q$. To enable incremental inference in probabilistic programming, our work: (i) introduces the concept of a trace translator which adapts samples from $P$ into samples of $Q$, (ii) phrases this translation approach in the context of sequential Monte Carlo (SMC), which gives theoretical guarantees that the adapted samples converge to the distribution induced by $Q$, and (iii) shows how to obtain a concrete trace translator by establishing a correspondence between the random choices of the two probabilistic programs. We implemented our approach in two different probabilistic programming systems and showed that, compared to methods that sample the program $Q$ from scratch, incremental inference can lead to orders of magnitude increase in efficiency, depending on how closely related $P$ and $Q$ are.},\n}\n\n","author_short":["Cusumano-Towner, M. F.","Bichsel, B.","Gehr, T.","Vechev, M.","Mansinghka, V. 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