A computational model for complex systems analysis: Causality estimation
Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, an...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sinha, A.K. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Role of the Fyn −93A>G polymorphism (rs706895) in acute rejection after liver transplantation - Thude, Hansjörg ELSEVIER, 2015transfer abstract, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:423 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.physd.2021.132915 |
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| 520 | |a Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. | ||
| 520 | |a Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. | ||
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10.1016/j.physd.2021.132915 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001422.pica (DE-627)ELV054412846 (ELSEVIER)S0167-2789(21)00073-7 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid Sinha, A.K. verfasserin aut A computational model for complex systems analysis: Causality estimation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. Nonlinear dynamics Elsevier Coupling Elsevier Causality Elsevier Chaotic systems Elsevier Loparo, K.A. oth Enthalten in Elsevier Thude, Hansjörg ELSEVIER Role of the Fyn −93A>G polymorphism (rs706895) in acute rejection after liver transplantation 2015transfer abstract Amsterdam [u.a.] (DE-627)ELV018422527 volume:423 year:2021 pages:0 https://doi.org/10.1016/j.physd.2021.132915 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA AR 423 2021 0 |
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10.1016/j.physd.2021.132915 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001422.pica (DE-627)ELV054412846 (ELSEVIER)S0167-2789(21)00073-7 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid Sinha, A.K. verfasserin aut A computational model for complex systems analysis: Causality estimation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. Nonlinear dynamics Elsevier Coupling Elsevier Causality Elsevier Chaotic systems Elsevier Loparo, K.A. oth Enthalten in Elsevier Thude, Hansjörg ELSEVIER Role of the Fyn −93A>G polymorphism (rs706895) in acute rejection after liver transplantation 2015transfer abstract Amsterdam [u.a.] (DE-627)ELV018422527 volume:423 year:2021 pages:0 https://doi.org/10.1016/j.physd.2021.132915 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA AR 423 2021 0 |
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10.1016/j.physd.2021.132915 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001422.pica (DE-627)ELV054412846 (ELSEVIER)S0167-2789(21)00073-7 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid Sinha, A.K. verfasserin aut A computational model for complex systems analysis: Causality estimation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. Nonlinear dynamics Elsevier Coupling Elsevier Causality Elsevier Chaotic systems Elsevier Loparo, K.A. oth Enthalten in Elsevier Thude, Hansjörg ELSEVIER Role of the Fyn −93A>G polymorphism (rs706895) in acute rejection after liver transplantation 2015transfer abstract Amsterdam [u.a.] (DE-627)ELV018422527 volume:423 year:2021 pages:0 https://doi.org/10.1016/j.physd.2021.132915 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA AR 423 2021 0 |
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10.1016/j.physd.2021.132915 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001422.pica (DE-627)ELV054412846 (ELSEVIER)S0167-2789(21)00073-7 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid Sinha, A.K. verfasserin aut A computational model for complex systems analysis: Causality estimation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. Nonlinear dynamics Elsevier Coupling Elsevier Causality Elsevier Chaotic systems Elsevier Loparo, K.A. oth Enthalten in Elsevier Thude, Hansjörg ELSEVIER Role of the Fyn −93A>G polymorphism (rs706895) in acute rejection after liver transplantation 2015transfer abstract Amsterdam [u.a.] (DE-627)ELV018422527 volume:423 year:2021 pages:0 https://doi.org/10.1016/j.physd.2021.132915 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA AR 423 2021 0 |
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10.1016/j.physd.2021.132915 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001422.pica (DE-627)ELV054412846 (ELSEVIER)S0167-2789(21)00073-7 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid Sinha, A.K. verfasserin aut A computational model for complex systems analysis: Causality estimation 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. Nonlinear dynamics Elsevier Coupling Elsevier Causality Elsevier Chaotic systems Elsevier Loparo, K.A. oth Enthalten in Elsevier Thude, Hansjörg ELSEVIER Role of the Fyn −93A>G polymorphism (rs706895) in acute rejection after liver transplantation 2015transfer abstract Amsterdam [u.a.] (DE-627)ELV018422527 volume:423 year:2021 pages:0 https://doi.org/10.1016/j.physd.2021.132915 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA AR 423 2021 0 |
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Enthalten in Role of the Fyn −93A>G polymorphism (rs706895) in acute rejection after liver transplantation Amsterdam [u.a.] volume:423 year:2021 pages:0 |
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Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. |
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Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. |
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Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types. |
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