Adaptive formal approximations of Markov chains

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU140793" target="_blank" >RIV/00216305:26230/21:PU140793 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0166531621000249" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0166531621000249</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.peva.2021.102207" target="_blank" >10.1016/j.peva.2021.102207</a>

Result language
angličtina
Original language name
Adaptive formal approximations of Markov chains
Original language description
We explore formal approximation techniques for Markov chains based on state-space reduction that aim at improving the scalability of the analysis, while providing formal bounds on the approximation error. We first present a comprehensive survey of existing state-reduction techniques based on clustering or truncation. Then, we extend existing frameworks for aggregation-based analysis of Markov chains by allowing them to handle chains with an arbitrary structure of the underlying state space - including continuous-time models - and improve upon existing bounds on the approximation error. Finally, we introduce a new hybrid scheme that utilises both aggregation and truncation of the state space and provides the best available approach for approximating continuous-time models. We conclude with a broad and detailed comparative evaluation of existing and new approximation techniques and investigate how different methods handle various Markov models. The results also show that the introduced hybrid scheme significantly outperforms existing approaches and provides a speedup of the analysis up to a factor of 30 with the corresponding approximation error bounded within 0.1%.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GJ20-02328Y" target="_blank" >GJ20-02328Y: CAQtuS: Computer-Aided Quantitative Synthesis</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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