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Non-parametric Bayesian networks: Improving theory and reviewing applications

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  • Hanea, Anca
  • Morales Napoles, Oswaldo
  • Ababei, Dan

Abstract

Applications in various domains often lead to high dimensional dependence modelling. A Bayesian network (BN) is a probabilistic graphical model that provides an elegant way of expressing the joint distribution of a large number of interrelated variables. BNs have been successfully used to represent uncertain knowledge in a variety of fields. The majority of applications use discrete BNs, i.e. BNs whose nodes represent discrete variables. Integrating continuous variables in BNs is an area fraught with difficulty. Several methods that handle discrete-continuous BNs have been proposed in the literature. This paper concentrates only on one method called non-parametric BNs (NPBNs). NPBNs were introduced in 2004 and they have been or are currently being used in at least twelve professional applications. This paper provides a short introduction to NPBNs, a couple of theoretical advances, and an overview of applications. The aim of the paper is twofold: one is to present the latest improvements of the theory underlying NPBNs, and the other is to complement the existing overviews of BNs applications with the NPNBs applications. The latter opens the opportunity to discuss some difficulties that applications pose to the theoretical framework and in this way offers some NPBN modelling guidance to practitioners.

Suggested Citation

  • Hanea, Anca & Morales Napoles, Oswaldo & Ababei, Dan, 2015. "Non-parametric Bayesian networks: Improving theory and reviewing applications," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 265-284.
    • Handle: RePEc:eee:reensy:v:144:y:2015:i:c:p:265-284
    DOI: 10.1016/j.ress.2015.07.027
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    References listed on IDEAS

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    Cited by:

    1. Werner, Christoph & Bedford, Tim & Cooke, Roger M. & Hanea, Anca M. & Morales-Nápoles, Oswaldo, 2017. "Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions," European Journal of Operational Research, Elsevier, vol. 258(3), pages 801-819.
    2. Daniel Puig & Oswaldo Morales-Nápoles & Fatemeh Bakhtiari & Gissela Landa, 2017. "The accountability imperative for quantifiying the uncertainty of emission forecasts : evidence from Mexico," Working Papers hal-03389325, HAL.
    3. Roger M Cooke & Bruce Wielicki, 2018. "Probabilistic reasoning about measurements of equilibrium climate sensitivity: combining disparate lines of evidence," Climatic Change, Springer, vol. 151(3), pages 541-554, December.
    4. Dominik Paprotny & Heidi Kreibich & Oswaldo Morales-Nápoles & Dennis Wagenaar & Attilio Castellarin & Francesca Carisi & Xavier Bertin & Bruno Merz & Kai Schröter, 2021. "A probabilistic approach to estimating residential losses from different flood types," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(3), pages 2569-2601, February.
    5. Qazi, Abroon & Simsekler, Mecit Can Emre, 2023. "Nexus between drivers of COVID-19 and country risks," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    6. repec:hal:spmain:info:hdl:2441/5cu79nktr182k9k26ecvt6f8p2 is not listed on IDEAS
    7. Zwirglmaier, Kilian & Straub, Daniel, 2016. "A discretization procedure for rare events in Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 96-109.
    8. Rajabzadeh, Vida & Hekmatzadeh, Ali Akbar & Tabatabaie Shourijeh, Piltan & Torabi Haghighi, Ali, 2023. "Introducing a probabilistic framework to measure dam overtopping risk for dams benefiting from dual spillways," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    9. Daniel Puig & Oswaldo Morales-Nápoles & Fatemeh Bakhtiari & Gissela Landa, 2017. "The accountability imperative for quantifiying the uncertainty of emission forecasts : evidence from Mexico," SciencePo Working papers Main hal-03389325, HAL.
    10. Daniel PUIG & Oswaldo Morales-Napoles & Fatemeh Bakhtiari & Gissela Landa Rivera, 2017. "The accountability imperative for quantifying the uncertainty of emission forecasts : evidence from Mexico," Documents de Travail de l'OFCE 2017-17, Observatoire Francais des Conjonctures Economiques (OFCE).
    11. Zhao, Tengyuan & Wang, Yu, 2020. "Non-parametric simulation of non-stationary non-gaussian 3D random field samples directly from sparse measurements using signal decomposition and Markov Chain Monte Carlo (MCMC) simulation," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    12. Daniel Puig & Oswaldo Morales Napoles & Fatemeh Bakhtiari & Gissela Landa, 2017. "The accountability imperative for quantifiying the uncertainty of emission forecasts : evidence from Mexico," Sciences Po publications info:hdl:2441/5cu79nktr18, Sciences Po.
    13. Nogal, Maria & Morales Nápoles, Oswaldo & O’Connor, Alan, 2019. "Structured expert judgement to understand the intrinsic vulnerability of traffic networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 127(C), pages 136-152.
    14. Wang, Fan & Li, Heng & Dong, Chao & Ding, Lieyun, 2019. "Knowledge representation using non-parametric Bayesian networks for tunneling risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 191(C).

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