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Conditioning in Probabilistic Programming

Jansen, N; Kaminski, BL; Katoen, JP; Olmedo, F; Gretz, F; McIver, A; (2015) Conditioning in Probabilistic Programming. In: Electronic Notes in Theoretical Computer Science. (pp. pp. 199-216). Elsevier Green open access

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Abstract

In this paper, we investigate the semantic intricacies of conditioning in probabilistic programming, a major feature, e.g., in machine learning. We provide a quantitative weakest pre-condition semantics. In contrast to all other approaches, non-termination is taken into account by our semantics. We also present an operational semantics in terms of Markov models and show that expected rewards coincide with quantitative pre-conditions. A program transformation that entirely eliminates conditioning from programs is given; the correctness is shown using our semantics. Finally, we show that an inductive semantics for conditioning in non-deterministic probabilistic programs cannot exist.

Type: Proceedings paper
Title: Conditioning in Probabilistic Programming
Event: The 31st Conference on the Mathematical Foundations of Programming Semantics (MFPS XXXI).
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.entcs.2015.12.013
Publisher version: https://doi.org/10.1016/j.entcs.2015.12.013
Language: English
Additional information: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: Probabilistic Programming, , SemanticsConditional Probabilities, Program Transformation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10094732
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