Stochastic pruning and its application for fast estimation of the expected total output of complex systems
- Submitting institution
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Oxford Brookes University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 185747240
- Type
- D - Journal article
- DOI
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10.1016/j.entcs.2016.09.026
- Title of journal
- Electronic Notes in Theoretical Computer Science
- Article number
- -
- First page
- 109
- Volume
- 327
- Issue
- -
- ISSN
- 1571-0661
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2016
- URL
-
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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0
- Research group(s)
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- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduces the powerful method of ‘stochastic pruning’ for analysing the performance of complex systems. Stochastic pruning is about introducing ultra-fast algorithms, whose computational speed is billions of times greater than the computational speed of conventional discrete event-simulators. The computation speed of the stochastic pruning algorithms does not depend on the length of the operating interval or the failure frequencies of the components.
- Author contribution statement
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- Non-English
- No
- English abstract
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