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Bayesian network models for incomplete and dynamic data

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  • Marco Scutari

Abstract

Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, in this paper, we will review how Bayesian networks can model dynamic data and data with incomplete observations. Such data are the norm at the forefront of research and in practical applications, and Bayesian networks are uniquely positioned to model them due to their explainability and interpretability.

Suggested Citation

  • Marco Scutari, 2020. "Bayesian network models for incomplete and dynamic data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 397-419, August.
    • Handle: RePEc:bla:stanee:v:74:y:2020:i:3:p:397-419
    DOI: 10.1111/stan.12197
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    References listed on IDEAS

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    1. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    2. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
    3. Mahdi Shafiee Kamalabad & Marco Grzegorczyk, 2018. "Improving nonhomogeneous dynamic Bayesian networks with sequentially coupled parameters," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 281-305, August.
    4. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    5. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    6. Gupta, Sumeet & Kim, Hee W., 2008. "Linking structural equation modeling to Bayesian networks: Decision support for customer retention in virtual communities," European Journal of Operational Research, Elsevier, vol. 190(3), pages 818-833, November.
    7. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    8. Rosa F. Ropero & M. Julia Flores & Rafael Rumí & Pedro A. Aguilera, 2017. "Applications of hybrid dynamic Bayesian networks to water reservoir management," Environmetrics, John Wiley & Sons, Ltd., vol. 28(1), February.
    9. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    10. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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