Accelerating simulation of Population Continuous Time Markov Chains via automatic model reduction
We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Feng, Cheng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
16 |
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| Übergeordnetes Werk: |
Enthalten in: Magnetic and spectroscopic characterizations of high-spin cobalt(II) complex with soft-scorpionate ligand - 2012transfer abstract, an international journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:120 ; year:2018 ; pages:20-35 ; extent:16 |
| Links: |
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| DOI / URN: |
10.1016/j.peva.2017.11.004 |
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| 520 | |a We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. | ||
| 520 | |a We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. | ||
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10.1016/j.peva.2017.11.004 doi GBV00000000000148A.pica (DE-627)ELV042111021 (ELSEVIER)S0166-5316(17)30130-X DE-627 ger DE-627 rakwb eng 600 600 DE-600 540 VZ Feng, Cheng verfasserin aut Accelerating simulation of Population Continuous Time Markov Chains via automatic model reduction 2018transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. Stochastic simulation Elsevier Model reduction Elsevier Population Continuous Time Markov Chain Elsevier Hillston, Jane oth Enthalten in Elsevier Magnetic and spectroscopic characterizations of high-spin cobalt(II) complex with soft-scorpionate ligand 2012transfer abstract an international journal Amsterdam [u.a.] (DE-627)ELV026258609 volume:120 year:2018 pages:20-35 extent:16 https://doi.org/10.1016/j.peva.2017.11.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_72 AR 120 2018 20-35 16 045F 600 |
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10.1016/j.peva.2017.11.004 doi GBV00000000000148A.pica (DE-627)ELV042111021 (ELSEVIER)S0166-5316(17)30130-X DE-627 ger DE-627 rakwb eng 600 600 DE-600 540 VZ Feng, Cheng verfasserin aut Accelerating simulation of Population Continuous Time Markov Chains via automatic model reduction 2018transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. Stochastic simulation Elsevier Model reduction Elsevier Population Continuous Time Markov Chain Elsevier Hillston, Jane oth Enthalten in Elsevier Magnetic and spectroscopic characterizations of high-spin cobalt(II) complex with soft-scorpionate ligand 2012transfer abstract an international journal Amsterdam [u.a.] (DE-627)ELV026258609 volume:120 year:2018 pages:20-35 extent:16 https://doi.org/10.1016/j.peva.2017.11.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_72 AR 120 2018 20-35 16 045F 600 |
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10.1016/j.peva.2017.11.004 doi GBV00000000000148A.pica (DE-627)ELV042111021 (ELSEVIER)S0166-5316(17)30130-X DE-627 ger DE-627 rakwb eng 600 600 DE-600 540 VZ Feng, Cheng verfasserin aut Accelerating simulation of Population Continuous Time Markov Chains via automatic model reduction 2018transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. Stochastic simulation Elsevier Model reduction Elsevier Population Continuous Time Markov Chain Elsevier Hillston, Jane oth Enthalten in Elsevier Magnetic and spectroscopic characterizations of high-spin cobalt(II) complex with soft-scorpionate ligand 2012transfer abstract an international journal Amsterdam [u.a.] (DE-627)ELV026258609 volume:120 year:2018 pages:20-35 extent:16 https://doi.org/10.1016/j.peva.2017.11.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_72 AR 120 2018 20-35 16 045F 600 |
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10.1016/j.peva.2017.11.004 doi GBV00000000000148A.pica (DE-627)ELV042111021 (ELSEVIER)S0166-5316(17)30130-X DE-627 ger DE-627 rakwb eng 600 600 DE-600 540 VZ Feng, Cheng verfasserin aut Accelerating simulation of Population Continuous Time Markov Chains via automatic model reduction 2018transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. Stochastic simulation Elsevier Model reduction Elsevier Population Continuous Time Markov Chain Elsevier Hillston, Jane oth Enthalten in Elsevier Magnetic and spectroscopic characterizations of high-spin cobalt(II) complex with soft-scorpionate ligand 2012transfer abstract an international journal Amsterdam [u.a.] (DE-627)ELV026258609 volume:120 year:2018 pages:20-35 extent:16 https://doi.org/10.1016/j.peva.2017.11.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_72 AR 120 2018 20-35 16 045F 600 |
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10.1016/j.peva.2017.11.004 doi GBV00000000000148A.pica (DE-627)ELV042111021 (ELSEVIER)S0166-5316(17)30130-X DE-627 ger DE-627 rakwb eng 600 600 DE-600 540 VZ Feng, Cheng verfasserin aut Accelerating simulation of Population Continuous Time Markov Chains via automatic model reduction 2018transfer abstract 16 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. Stochastic simulation Elsevier Model reduction Elsevier Population Continuous Time Markov Chain Elsevier Hillston, Jane oth Enthalten in Elsevier Magnetic and spectroscopic characterizations of high-spin cobalt(II) complex with soft-scorpionate ligand 2012transfer abstract an international journal Amsterdam [u.a.] (DE-627)ELV026258609 volume:120 year:2018 pages:20-35 extent:16 https://doi.org/10.1016/j.peva.2017.11.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_72 AR 120 2018 20-35 16 045F 600 |
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accelerating simulation of population continuous time markov chains via automatic model reduction |
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Accelerating simulation of Population Continuous Time Markov Chains via automatic model reduction |
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We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. |
| abstractGer |
We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. |
| abstract_unstemmed |
We present a novel model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. Specifically, given a set of predefined target populations of the modellers’ interest, our method exploits the coupling coefficients between population variables and transitions with respect to those target populations which are calculated based on a directed coupling graph constructed for the PCTMC. Population variables and transitions which have high coupling coefficients on the target populations are exactly simulated. However, the remaining population variables and transitions which have low coupling coefficients can either be removed or approximately simulated in the reduced model. The reduced model generated by our approach has significantly lower cost for stochastic simulation, but still retains high accuracy on the statistical properties of the target populations. The applicability and effectiveness of our method are demonstrated on two illustrative models. |
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GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_72 |
| title_short |
Accelerating simulation of Population Continuous Time Markov Chains via automatic model reduction |
| url |
https://doi.org/10.1016/j.peva.2017.11.004 |
| remote_bool |
true |
| author2 |
Hillston, Jane |
| author2Str |
Hillston, Jane |
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| doi_str |
10.1016/j.peva.2017.11.004 |
| up_date |
2024-07-06T21:56:06.095Z |
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