Zobrazeno 1 - 10
of 36 108
pro vyhledávání: '"model: statistical"'
We introduce a family of parsimonious network models that are intended to generalize the configuration model to temporal settings. We present consistent estimators for the model parameters and perform numerical simulations to illustrate the propertie
Externí odkaz:
http://arxiv.org/abs/2407.12175
Akademický článek
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Publikováno v:
AIMS Mathematics, Vol 9, Iss 8, Pp 22813-22841 (2024)
In this paper, we proposed a novel probability distribution model known as the unit compound Rayleigh distribution, which possesses the distinctive characteristic of defining the range within the bounded interval (0, 1). Through an in-depth investiga
Externí odkaz:
https://doaj.org/article/987c2c1a167c4ed18bc92e2a7f11b8cd
Publikováno v:
In Chemosphere September 2023 335
Autor:
Müller, T. S., Pastor, G. M.
The ground-state properties of the Hubbard model with attractive local pairing interactions are investigated in the framework of lattice density-functional theory. A remarkable correlation is revealed between the interaction-energy functional $W[\bol
Externí odkaz:
http://arxiv.org/abs/2110.07422
Autor:
Huang, Bo a, ⁎, Li, Yan b, ⁎, Liu, Yi c, Hu, Xiangping a, Zhao, Wenwu d, Cherubini, Francesco a
Publikováno v:
In Agricultural and Forest Meteorology 1 April 2023 332
Autor:
Timonin, P. N.
Publikováno v:
Proceedings: Mathematical, Physical and Engineering Sciences, 2020 Aug 01. 476(2240), 1-14.
Externí odkaz:
https://www.jstor.org/stable/27097211
Publikováno v:
Benito R.M., Cherifi C., Cherifi H., Moro E., Rocha L.M., Sales-Pardo M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 944. Springer, Cham
Community detection in graphs often relies on ad hoc algorithms with no clear specification about the node partition they define as the best, which leads to uninterpretable communities. Stochastic block models (SBM) offer a framework to rigorously de
Externí odkaz:
http://arxiv.org/abs/2106.13571
Autor:
Yevick, David
Variational autoencoders employ an encoding neural network to generate a probabilistic representation of a data set within a low-dimensional space of latent variables followed by a decoding stage that maps the latent variables back to the original va
Externí odkaz:
http://arxiv.org/abs/2104.06368
Akademický článek
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