Causality based graph structure of stochastic linear state-space representations

M. Jozsa; M. Petreczky; M. K. Camlibel

doi:10.1109/CDC.2017.8264007

Causality based graph structure of stochastic linear state-space representations

M. Jozsa
, M. Petreczky
, M. K. Camlibel

Systems, Control and Applied Analysis

Research output: Contribution to conference › Paper › Academic

3 Citations (Scopus)

Abstract

This paper deals with different approaches to interpret the connectivity structure of complex systems. We connect the graph structure of linear state-space representations of a weakly stationary stochastic process and the non-causal relations between its components.

Original language	English
Pages	2442-2447
Number of pages	6
DOIs	https://doi.org/10.1109/CDC.2017.8264007
Publication status	Published - 2017
Event	56th IEEE Conference on Decision and Control - Melbourne, Australia Duration: 12-Dec-2017 → 15-Dec-2017

Conference

Conference	56th IEEE Conference on Decision and Control
Country/Territory	Australia
City	Melbourne
Period	12/12/2017 → 15/12/2017

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Causality based graph structure of stochastic linear state-space representations
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title = "Causality based graph structure of stochastic linear state-space representations",

abstract = "This paper deals with different approaches to interpret the connectivity structure of complex systems. We connect the graph structure of linear state-space representations of a weakly stationary stochastic process and the non-causal relations between its components.",

author = "M. Jozsa and M. Petreczky and Camlibel, \{M. K.\}",

year = "2017",

doi = "10.1109/CDC.2017.8264007",

language = "English",

pages = "2442--2447",

note = "56th IEEE Conference on Decision and Control ; Conference date: 12-12-2017 Through 15-12-2017",

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AU - Jozsa, M.

AU - Petreczky, M.

AU - Camlibel, M. K.

PY - 2017

Y1 - 2017

N2 - This paper deals with different approaches to interpret the connectivity structure of complex systems. We connect the graph structure of linear state-space representations of a weakly stationary stochastic process and the non-causal relations between its components.

AB - This paper deals with different approaches to interpret the connectivity structure of complex systems. We connect the graph structure of linear state-space representations of a weakly stationary stochastic process and the non-causal relations between its components.

U2 - 10.1109/CDC.2017.8264007

DO - 10.1109/CDC.2017.8264007

M3 - Paper

SP - 2442

EP - 2447

T2 - 56th IEEE Conference on Decision and Control

Y2 - 12 December 2017 through 15 December 2017

ER -

Causality based graph structure of stochastic linear state-space representations

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