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Theory and Practice of Risk Assessment pp 159–175Cite as

Risk Assessment of Complex Evolving Systems Involving Multiple Inputs

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Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 136))

Abstract

When monitoring complex evolving systems a question that often arises is to detect causal chains of events. Do particular inputs force the system to produce new events or prohibits them? This can be also considered as a risk assessment for the systems response. In this work we present two methods of estimating the effect of multiple inputs on a complex neurophysiological system. Both the response and the stimuli are very long binary time series. The first approach is a non-parametric one and describes the linear and the non-linear association of the stationary point processes by estimating the second- and third-order cumulant density functions. The second approach is a parametric one and the association between the inputs and the response of the system is described by a logistic regression model which takes into account the system’s internal processes.

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Notes

  1. 1.

    The word ‘stochastic’ is implied.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Democritus University of Thrace, GR67100, Xanthi, Greece

    A. G. Rigas & V. G. Vassiliadis

Authors
  1. A. G. Rigas
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  2. V. G. Vassiliadis
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Correspondence to A. G. Rigas .

Editor information

Editors and Affiliations

  1. Technological Educational Institute of Athens, Athens, Greece

    Christos P. Kitsos

  2. Universidade Aberta, Palácio Ceia, Lisbon, Portugal

    Teresa A. Oliveira

  3. Democritus University, Thrace, Germany

    Alexandros Rigas

  4. Florida International University, Miami, Florida, USA

    Sneh Gulati

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© 2015 Springer International Publishing Switzerland

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Rigas, A.G., Vassiliadis, V.G. (2015). Risk Assessment of Complex Evolving Systems Involving Multiple Inputs. In: Kitsos, C., Oliveira, T., Rigas, A., Gulati, S. (eds) Theory and Practice of Risk Assessment. Springer Proceedings in Mathematics & Statistics, vol 136. Springer, Cham. https://doi.org/10.1007/978-3-319-18029-8_13

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