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International Conference on Machine Learning, Optimization, and Data Science

LOD 2018: Machine Learning, Optimization, and Data Science pp 179–190Cite as

Learning Consistent Tree-Augmented Dynamic Bayesian Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11331))

Abstract

Dynamic Bayesian networks (DBNs) offer an approach that allows for causal and temporal dependencies between random variables repeatedly measured over time. For this reason, they have been used in several domains such as medical prognostic predictions, meteorology and econometrics. Learning the intra-slice dependencies is, however, most of the times neglected. This is due to the inherent difficulty in dealing with cyclic dependencies. We propose an algorithm for learning optimal DBNs consistent with the tree-augmented network (tDBN). This algorithm uses the topological order induced by the tDBN to increase its search space exponentially while keeping the time complexity polynomial.

This work was supported through FCT, under contract IT (UID/EEA/50008/2013), and by projects PERSEIDS (PTDC/EMS-SIS/0642/2014), NEUROCLINOMICS2 (PTDC/EEI-SII/1937/2014), and internal IT projects QBigData and RAPID.

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Notes

  1. 1.

    https://margaridanarsousa.github.io/learn_cDBN/.

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Author information

Authors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Margarida Sousa & Alexandra M. Carvalho

  2. Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal

    Margarida Sousa & Alexandra M. Carvalho

Authors
  1. Margarida Sousa
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  2. Alexandra M. Carvalho
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Corresponding author

Correspondence to Alexandra M. Carvalho .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy and University of Reading, Reading, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

  5. IBM, Tivoli Research Lab, Rome, Italy

    Vincenzo Sciacca

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Sousa, M., Carvalho, A.M. (2019). Learning Consistent Tree-Augmented Dynamic Bayesian Networks. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_15

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  • DOI: https://doi.org/10.1007/978-3-030-13709-0_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13708-3

  • Online ISBN: 978-3-030-13709-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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