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How long, O Bayesian network, will I sample thee?: A program analysis perspective on expected sampling times

Batz, K; Kaminski, BL; Katoen, JP; Matheja, C; (2018) How long, O Bayesian network, will I sample thee?: A program analysis perspective on expected sampling times. In: Ahmed, A, (ed.) Proceedings of Programming Languages and Systems. ESOP 2018. (pp. pp. 186-213). Springer: Cham. Green open access

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Abstract

© The Author(s) 2018. Bayesian networks (BNs) are probabilistic graphical models for describing complex joint probability distributions. The main problem for BNs is inference: Determine the probability of an event given observed evidence. Since exact inference is often infeasible for large BNs, popular approximate inference methods rely on sampling. We study the problem of determining the expected time to obtain a single valid sample from a BN. To this end, we translate the BN together with observations into a probabilistic program. We provide proof rules that yield the exact expected runtime of this program in a fully automated fashion. We implemented our approach and successfully analyzed various real–world BNs taken from the Bayesian network repository.

Type: Proceedings paper
Title: How long, O Bayesian network, will I sample thee?: A program analysis perspective on expected sampling times
Event: Programming Languages and Systems. ESOP 2018.
ISBN-13: 978-3-319-89883-4
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-89884-1_7
Publisher version: https://doi.org/10.1007/978-3-319-89884-1_7
Language: English
Additional information: Copyright information © The Author(s) 2018 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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/10089702
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