Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 1415-1423 |
| Seitenumfang | 9 |
| Fachzeitschrift | Soft Computing |
| Jahrgang | 17 |
| Ausgabenummer | 8 |
| Frühes Online-Datum | 26 Feb. 2013 |
| Publikationsstatus | Veröffentlicht - Aug. 2013 |
| Extern publiziert | Ja |
Abstract
Markov chains provide quite attractive features for simulating a system's behavior under consideration of uncertainties. However, their use is somewhat limited because of their deterministic transition matrices. Vague probabilistic information and imprecision appear in the modeling of real-life systems, thus causing difficulties in the pure probabilistic model set-up. Moreover, their accuracy suffers due to implementations on computers with floating point arithmetics. Our goal is to address these problems by extending the Dempster-Shafer with Intervals toolbox for MATLAB with novel verified algorithms for modeling that work with Markov chains with imprecise transition matrices, known as Markov set-chains. Additionally, in order to provide a statistical estimation tool that can handle imprecision to set up Markov chain models, we develop a new verified algorithm for computing relations between the mean and the standard deviation of fuzzy sets.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Mathematik (insg.)
- Theoretische Informatik
- Mathematik (insg.)
- Geometrie und Topologie
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in: Soft Computing, Jahrgang 17, Nr. 8, 08.2013, S. 1415-1423.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Verified stochastic methods
T2 - Markov set-chains and dependency modeling of mean and standard deviation
AU - Rebner, Gabor
AU - Beer, Michael
AU - Auer, Ekaterina
AU - Stein, Matthias
PY - 2013/8
Y1 - 2013/8
N2 - Markov chains provide quite attractive features for simulating a system's behavior under consideration of uncertainties. However, their use is somewhat limited because of their deterministic transition matrices. Vague probabilistic information and imprecision appear in the modeling of real-life systems, thus causing difficulties in the pure probabilistic model set-up. Moreover, their accuracy suffers due to implementations on computers with floating point arithmetics. Our goal is to address these problems by extending the Dempster-Shafer with Intervals toolbox for MATLAB with novel verified algorithms for modeling that work with Markov chains with imprecise transition matrices, known as Markov set-chains. Additionally, in order to provide a statistical estimation tool that can handle imprecision to set up Markov chain models, we develop a new verified algorithm for computing relations between the mean and the standard deviation of fuzzy sets.
AB - Markov chains provide quite attractive features for simulating a system's behavior under consideration of uncertainties. However, their use is somewhat limited because of their deterministic transition matrices. Vague probabilistic information and imprecision appear in the modeling of real-life systems, thus causing difficulties in the pure probabilistic model set-up. Moreover, their accuracy suffers due to implementations on computers with floating point arithmetics. Our goal is to address these problems by extending the Dempster-Shafer with Intervals toolbox for MATLAB with novel verified algorithms for modeling that work with Markov chains with imprecise transition matrices, known as Markov set-chains. Additionally, in order to provide a statistical estimation tool that can handle imprecision to set up Markov chain models, we develop a new verified algorithm for computing relations between the mean and the standard deviation of fuzzy sets.
KW - DSI
KW - Imprecise probability
KW - Interval arithmetic
KW - Markov set-chains
UR - http://www.scopus.com/inward/record.url?scp=84880807695&partnerID=8YFLogxK
U2 - 10.1007/s00500-013-1009-7
DO - 10.1007/s00500-013-1009-7
M3 - Article
AN - SCOPUS:84880807695
VL - 17
SP - 1415
EP - 1423
JO - Soft Computing
JF - Soft Computing
SN - 1432-7643
IS - 8
ER -