Results 61 to 70 of about 128,370 (210)
Maximum entropy modelling of sub-optimal transport
Communications PhysicsMany natural systems involve structures shaped by competing forces: efficiency, randomness, and incomplete information. To the best of our knowledge, there is currently no robust method to assess the presence of optimization processes in real networks ...
Lorenzo Buffa +5 more
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On Tsallis Entropy Bias and Generalized Maximum Entropy Models [PDF]
, 2010 Yuexian Hou +4 more
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Entropy, 2017 The determination of the probability distribution function (PDF) of uncertain input and model parameters in engineering application codes is an issue of importance for uncertainty quantification methods. One of the approaches that can be used for the PDF
José-Luis Muñoz-Cobo +4 more
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Rumor Identification with Maximum Entropy in MicroNet
Complexity, 2017 The widely used applications of Microblog, WeChat, and other social networking platforms (that we call MicroNet) shorten the period of information dissemination and expand the range of information dissemination, which allows rumors to cause greater harm ...
Suisheng Yu, Mingcai Li, Fengming Liu
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A Nested Test for Common Yield Distributions with Applications to U.S. Corn
Journal of Agricultural and Resource Economics, 2013 We propose the use of maximum-entropy techniques to nest and test the functional form of commonly used yield densities. We demonstrate how common parametric yield models can be nested within a maximum-entropy framework and subsequently tested for using ...
Jesse Tack
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Merging exchangeable occupancy models: $\\mathcal{M}^{(a)}$- models and\n relation with the maximum entropy principle [PDF]
, 2014 Francesca Collet +2 more
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Maximum entropy network states for coalescence processes [PDF]
, 2022 Arsham Ghavasieh, Manlio De Domenico
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The Maximum Entropy Fallacy Redux? [PDF]
PLoS Computational Biology, 2016 Erik Aurell
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Maximum average entropy-rate based correlation clustering for big data [PDF]
, 2019 Zongben Xu +4 more
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Maximum Entropy Discrimination [PDF]
, 2004 We present a general framework for discriminative estimation based on the maximum entropy principle and its extensions. All calculations involve distributions over structures and/or parameters rather than specific settings and reduce to relative entropy projections.
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