Výsledky vyhledávání

The hidden elegance of causal interaction models

Autor: Renooij, S., van der Gaag, L.C., Ben Amor, Nahla, Quost, Benjamin, Theobald, Martin, Intelligent Systems, Decision Support Systems, Sub Intelligent Systems, Sub Decision Support Systems

Publikováno v: Scalable Uncertainty Management, 11940(1), 38. Springer Cham
Lecture Notes in Computer Science ISBN: 9783030355135
SUM

Causal interaction models such as the noisy-or model, are used in Bayesian networks to simplify probability acquisition for variables with large numbers of modelled causes. These models essentially prescribe how to complete an exponentially large pro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c5a1f03563a127bad9fe58b33c3532af
https://dspace.library.uu.nl/handle/1874/390095

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Supporting Discussions About Forensic Bayesian Networks Using Argumentation

Autor: Wieten, G.M., Bex, F.J., Prakken, H., Renooij, S., Sub Intelligent Systems, Sub Decision Support Systems, Intelligent Systems, Decision Support Systems

Publikováno v: ICAIL
ICAIL '19, 143. Association for Computing Machinery (ACM)
STARTPAGE=143;TITLE=ICAIL '19
Seventeenth International Conference on Artificial Intelligence and Law: proceedings of the conference, 143-152
STARTPAGE=143;ENDPAGE=152;TITLE=Seventeenth International Conference on Artificial Intelligence and Law
ICAIL '19: Proceedings of the seventeenth International Conference on Artificial Intelligence and Law, 143-152
STARTPAGE=143;ENDPAGE=152;TITLE=ICAIL '19

Bayesian networks (BNs) are powerful tools that are increasingly being used by forensic and legal experts to reason about the uncertain conclusions that can be inferred from the evidence in a case. Although in BN construction it is good practice to d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59bca792333c1ca5b837e661f631094a
https://hdl.handle.net/1874/386079

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On Intercausal Interactions in Probabilistic Relational Models

Autor: Renooij, S., van der Gaag, L.C., Leray, Ph., De Bock, Jasper, de Campos, Cassio P., de Cooman, Gert, Quaeghebeur, Erik, Wheeler, Gregory

Probabilistic relational models (PRMs) extend Bayesian networks beyond propositional expressiveness by allowing the representation of multiple interacting classes. For a specific instance of sets of concrete objects per class, a ground Bayesian netwo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______101::4147ac55281c0bb391797f059e8e5344
https://dspace.library.uu.nl/handle/1874/427792

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Akademický článek

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A method for explaining Bayesian networks for legal evidence with scenarios

Autor: Vlek, Charlotte, Prakken, H., Renooij, S., Verheij, B., Sub Intelligent Systems, Sub Decision Support Systems, Intelligent Systems, Decision Support Systems

Publikováno v: Artificial Intelligence and Law, 24(3), 285. Springer Netherlands
Artificial Intelligence and Law, 24(3), 285-324. Springer

In a criminal trial, a judge or jury needs to reason about what happenedbased on the available evidence, often including statistical evidence. While aprobabilistic approach is suitable for analysing the statistical evidence, a judge orjury may be mor

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8df618552ebf95d6b204e36d9c546b17
https://doi.org/10.1007/s10506-016-9183-4

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Same-Decision Probability: threshold robustness and application to explanation

Autor: Renooij, S., Kratochvíl, Václav, Studený, Milan, Decision Support Systems, Sub Decision Support Systems

Publikováno v: Proceedings of the Ninth International Conference on Probabilistic Graphical Models (PGM), 72, 368. PMLR

The same-decision probability (SDP) is a confidence measure for threshold-based decisions. In this paper we detail various properties of the SDP that allow for studying its robustness to changes in the threshold value upon which a decision is based.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::c0cae32330ce16f6880b329138f522ce
https://dspace.library.uu.nl/handle/1874/370179

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Refining a heuristic for constructing Bayesian networks from structured arguments

Autor: Wieten, G.M., Bex, F.J., van der Gaag, L.C., Prakken, H., Renooij, S., Verheij, Bart, Wiering, Marco

Recently, a heuristic was proposed for constructing Bayesian networks (BNs) from structured arguments. This heuristic helps domain experts who are accustomed to argumentation to transform their reasoning into a BN and subsequently weigh their case ev

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______101::84f7ddebe02ba7a198184e609b2db903
https://dspace.library.uu.nl/handle/1874/362882

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Discretizing environmental data for learning Bayesian-network classifiers

Autor: Fernandez Ropero, R.M., Renooij, S., van der Gaag, L.C., Sub Decision Support Systems, Decision Support Systems

Publikováno v: Ecological Modelling, 368, 391. Elsevier

For predicting the presence of different bird species in Andalusia from land-use data, we compare the performances of Bayesian-network classifiers and logistic-regression models. In our study, both well balanced and less balanced data sets are used,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6c6d5494a86e27f8fe05fcd2e29d5a5
https://dspace.library.uu.nl/handle/1874/362818

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