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Inferring Covariances for Probabilistic Programs

Kaminski, BL; Katoen, J-P; Matheja, C; (2016) Inferring Covariances for Probabilistic Programs. In: Agha, G and Van Houdt, B, (eds.) Quantitative Evaluation of Systems: 13th International Conference, QEST 2016, Quebec City, QC, Canada, August 23-25, 2016, Proceedings. (pp. pp. 191-206). Springer: Cham, Switzerland. Green open access

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Abstract

We study weakest precondition reasoning about the (co)variance of outcomes and the variance of run–times of probabilistic programs with conditioning. For outcomes, we show that approximating (co)variances is computationally more difficult than approximating expected values. In particular, we prove that computing both lower and upper bounds for (co)variances is Σ02–complete. As a consequence, neither lower nor upper bounds are computably enumerable. We therefore present invariant–based techniques that do enable enumeration of both upper and lower bounds, once appropriate invariants are found. Finally, we extend this approach to reasoning about run–time variances.

Type: Proceedings paper
Title: Inferring Covariances for Probabilistic Programs
Event: 13th International Conference on Quantitative Evaluation of Systems (QEST)
Location: Quebec City, CANADA
Dates: 23 August 2016 - 25 August 2016
ISBN-13: 978-3-319-43424-7
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-43425-4_14
Publisher version: https://doi.org/10.1007/978-3-319-43425-4_14
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Probabilistic programs, Covariance, Run–time
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/10089695
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