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A tutorial on uncertainty modeling for machine reasoning

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dc.contributor.author Ristic, Branko
dc.contributor.author Gilliam, Christopher
dc.contributor.author Byrne, Marion
dc.contributor.author Benavoli, Alessio
dc.date.accessioned 2019-09-09T10:46:47Z
dc.date.available 2019-09-09T10:46:47Z
dc.date.issued 2019
dc.identifier.uri http://hdl.handle.net/10344/8039
dc.description peer-reviewed en_US
dc.description.abstract Increasingly we rely on machine intelligence for reasoning and decision making under uncertainty. This tutorial reviews the prevalent methods for model-based autonomous decision making based on observations and prior knowledge, primarily in the context of classification. Both observations and the knowledge-base available for reasoning are treated as being uncertain. Accordingly, the central themes of this tutorial are quantitative mod- eling of uncertainty, the rules required to combine such uncertain information, and the task of decision making under uncertainty. The paper covers the main approaches to uncertain knowledge representation and reasoning, in particular, Bayesian probability theory, possibility theory, reasoning based on belief functions and finally im- precise probability theory. The main feature of the tutorial is that it illustrates various approaches with several testing scenarios, and provides MATLAB solutions for them as a supplementary material for an interested reader en_US
dc.language.iso eng en_US
dc.relation.ispartofseries Information Fusion;55(2020),pp. 30–44
dc.subject Information fusion en_US
dc.subject Uncertainty en_US
dc.subject Imprecision en_US
dc.subject Model based classification en_US
dc.subject Bayesian en_US
dc.title A tutorial on uncertainty modeling for machine reasoning en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1016/j.inffus.2019.08.001
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


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