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2018 | OriginalPaper | Buchkapitel

Qualitative Probabilistic Relational Models

verfasst von : Linda C. van der Gaag, Philippe Leray

Erschienen in: Scalable Uncertainty Management

Verlag: Springer International Publishing

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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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Titel: Qualitative Probabilistic Relational Models
verfasst von: Linda C. van der Gaag
Philippe Leray
Verlag: Springer International Publishing
Buch: Scalable Uncertainty Management
Print ISBN: 978-3-030-00460-6

Electronic ISBN: 978-3-030-00461-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00461-3_19

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