IDEAS home Printed from https://ideas.repec.org/h/ito/pchaps/159424.html
   My bibliography  Save this book chapter

Bayesian Networks for Decision-Making and Causal Analysis under Uncertainty in Aviation

In: Bayesian Networks - Advances and Novel Applications

Author

Listed:
  • Rosa Arnaldo
  • Victor Fernando Gomez Comendador
  • Alvaro Rodriguez Sanz
  • Eduardo Sanchez Ayra
  • Javier Alberto Perez Castan
  • Luis Perez Sanz

Abstract

Most decisions in aviation regarding systems and operation are currently taken under uncertainty, relaying in limited measurable information, and with little assistance of formal methods and tools to help decision makers to cope with all those uncertainties. This chapter illustrates how Bayesian analysis can constitute a systematic approach for dealing with uncertainties in aviation and air transport. The chapter addresses the three main ways in which Bayesian networks are currently employed for scientific or regulatory decision-making purposes in the aviation industry, depending on the extent to which decision makers rely totally or partially on formal methods. These three alternatives are illustrated with three aviation case studies that reflect research work carried out by the authors.

Suggested Citation

  • Rosa Arnaldo & Victor Fernando Gomez Comendador & Alvaro Rodriguez Sanz & Eduardo Sanchez Ayra & Javier Alberto Perez Castan & Luis Perez Sanz, 2019. "Bayesian Networks for Decision-Making and Causal Analysis under Uncertainty in Aviation," Chapters, in: Douglas McNair (ed.), Bayesian Networks - Advances and Novel Applications, IntechOpen.
    • Handle: RePEc:ito:pchaps:159424
    DOI: 10.5772/intechopen.79916
    as

    Download full text from publisher

    File URL: https://www.intechopen.com/chapters/62844
    Download Restriction: no
    File URL: https://libkey.io/10.5772/intechopen.79916?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    Bayesian networks; prediction; classification; risk; anomaly detection; causal modelling; uncertainty;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ito:pchaps:159424. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Slobodan Momcilovic (email available below). General contact details of provider: http://www.intechopen.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.