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Qualitative Probabilistic Relational Models

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Scalable Uncertainty Management (SUM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11142))

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Abstract

Probabilistic relational models (PRMs) were introduced to extend the modelling and reasoning capacities of Bayesian networks from propositional to relational domains. PRMs are typically learned from relational data, by extracting from these data both a dependency structure and its numerical parameters. For this purpose, a large and rich data set is required, which proves prohibitive for many real-world applications. Since a PRM’s structure can often be readily elicited from domain experts, we propose manual construction by an approach that combines qualitative concepts adapted from qualitative probabilistic networks (QPNs) with stepwise quantification. To this end, we introduce qualitative probabilistic relational models (QPRMs) and tailor an existing algorithm for qualitative probabilistic inference to these new models.

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Author information

Authors and Affiliations

  1. Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands

    Linda C. van der Gaag

  2. LS2N UMR CNRS 6004, DUKe research group, University of Nantes, Nantes, France

    Philippe Leray

Authors
  1. Linda C. van der Gaag
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  2. Philippe Leray
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Corresponding author

Correspondence to Linda C. van der Gaag .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

  2. University of Milano-Bicocca, Milan, Italy

    Gabriella Pasi

  3. Sapienza University of Rome, Rome, Italy

    Barbara Vantaggi

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van der Gaag, L.C., Leray, P. (2018). Qualitative Probabilistic Relational Models. In: Ciucci, D., Pasi, G., Vantaggi, B. (eds) Scalable Uncertainty Management. SUM 2018. Lecture Notes in Computer Science(), vol 11142. Springer, Cham. https://doi.org/10.1007/978-3-030-00461-3_19

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  • DOI: https://doi.org/10.1007/978-3-030-00461-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00460-6

  • Online ISBN: 978-3-030-00461-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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