Results 41 to 50 of about 672,665 (262)
Generalizing Information to the Evolution of Rational Belief
Entropy, 2020 Information theory provides a mathematical foundation to measure uncertainty in belief. Belief is represented by a probability distribution that captures our understanding of an outcome’s plausibility.
Jed A. Duersch, Thomas A. Catanach
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Belief-Logic Conflict Resolution in Syllogistic Reasoning: Inspection-Time Evidence for a Parallel-Process Model [PDF]
, 2008 An experiment is reported examining dual-process models of belief bias in syllogistic reasoning using a problem complexity manipulation and an inspection-time method to monitor processing latencies for premises and conclusions.
Stupple, Edward J. N. +3 more
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Acknowledgment to Reviewers of Entropy in 2020
, 2021 Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Entropy maintains its standards for the high quality of its published papers [...
Entropy Editorial Office Entropy Editorial Office
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Sensors, 2018 Quantification of uncertain degree in the Dempster-Shafer evidence theory (DST) framework with belief entropy is still an open issue, even a blank field for the open world assumption.
Yongchuan Tang +2 more
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Sensors, 2023 In IoT environments, voluminous amounts of data are produced every single second. Due to multiple factors, these data are prone to various imperfections, they could be uncertain, conflicting, or even incorrect leading to wrong decisions. Multisensor data
Nour El Imane Hamda +2 more
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Entropy and Belief Networks [PDF]
, 1992 The product expansion of conditional probabilities for belief nets is not maximum entropy. This appears to deny a desirable kind of assurance for the model. However, a kind of guarantee that is almost as strong as maximum entropy can be derived. Surprisingly, a variant model also exhibits the guarantee, and for many cases obtains a higher performance ...
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An Improved Total Uncertainty Measure in the Evidence Theory and Its Application in Decision Making
Entropy, 2020 Dempster–Shafer evidence theory (DS theory) has some superiorities in uncertain information processing for a large variety of applications. However, the problem of how to quantify the uncertainty of basic probability assignment (BPA) in DS theory ...
Miao Qin, Yongchuan Tang, Junhao Wen
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An Improved Deng Entropy and Its Application in Pattern Recognition
IEEE Access, 2019 How to manage the uncertainty of the basic probability assignment accurately and efficiently is of significance and also an open issue. Plenty of functions have been established to cover the issue, especially Deng entropy recently.
Huizi Cui +3 more
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Formal Analysis of Information Flow Using Min-Entropy and Belief Min-Entropy [PDF]
, 2013 Information flow analysis plays a vital role in obtaining quantitative bounds on information leakage due to external attacks. Traditionally, information flow analysis is done using paper-and-pencil based proofs or computer simulations based on the Shannon entropy and mutual information.
Ghassen Helali +2 more
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A Variation of the Algorithm to Achieve the Maximum Entropy for Belief Functions
Entropy, 2023 Evidence theory (TE), based on imprecise probabilities, is often more appropriate than the classical theory of probability (PT) to apply in situations with inaccurate or incomplete information. The quantification of the information that a piece of evidence involves is a key issue in TE.
Joaquín Abellán +2 more
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