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Part of book or chapter of book . 2014 . Peer-reviewed
Data sources: SNSF P3 Database
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Pure Utrecht University
Part of book or chapter of book . 2014
Data sources: Pure Utrecht University
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Probabilistic Graphical Models

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Article 01 Jan 2014 English Publisher:Wiley-BlackwellFunded by:SNSF | Learning under near-ignor...
Authors: Antonucci, Alessandro; de Campos, Cassio P.; Zaffalon, Marco;

Probabilistic Graphical Models

Abstract

This report presents probabilistic graphical models that are based on imprecise probabilities using a simplified language. In particular the discussion is focused on credal networks and discrete domains. It describes the building blocks of credal networks algorithms to perform inference and discusses on complexity results and related work. The goal is to have an easy to follow introduction to the topic.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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