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Auswahl der wissenschaftlichen Literatur zum Thema „Probability-Graphons“
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Zeitschriftenartikel zum Thema "Probability-Graphons"
Ackerman, Nate, Cameron E. Freer, Younesse Kaddar, Jacek Karwowski, Sean Moss, Daniel Roy, Sam Staton und Hongseok Yang. „Probabilistic Programming Interfaces for Random Graphs: Markov Categories, Graphons, and Nominal Sets“. Proceedings of the ACM on Programming Languages 8, POPL (05.01.2024): 1819–49. http://dx.doi.org/10.1145/3632903.
Der volle Inhalt der QuelleMcMillan, Audra, und Adam Smith. „When is non-trivial estimation possible for graphons and stochastic block models?‡“. Information and Inference: A Journal of the IMA 7, Nr. 2 (23.08.2017): 169–81. http://dx.doi.org/10.1093/imaiai/iax010.
Der volle Inhalt der QuelleZHAO, YUFEI. „On the Lower Tail Variational Problem for Random Graphs“. Combinatorics, Probability and Computing 26, Nr. 2 (16.08.2016): 301–20. http://dx.doi.org/10.1017/s0963548316000262.
Der volle Inhalt der QuelleBraides, Andrea, Paolo Cermelli und Simone Dovetta. „Γ-limit of the cut functional on dense graph sequences“. ESAIM: Control, Optimisation and Calculus of Variations 26 (2020): 26. http://dx.doi.org/10.1051/cocv/2019029.
Der volle Inhalt der QuelleHATAMI, HAMED, und SERGUEI NORINE. „The Entropy of Random-Free Graphons and Properties“. Combinatorics, Probability and Computing 22, Nr. 4 (16.05.2013): 517–26. http://dx.doi.org/10.1017/s0963548313000175.
Der volle Inhalt der QuelleKeliger, Dániel, Illés Horváth und Bálint Takács. „Local-density dependent Markov processes on graphons with epidemiological applications“. Stochastic Processes and their Applications 148 (Juni 2022): 324–52. http://dx.doi.org/10.1016/j.spa.2022.03.001.
Der volle Inhalt der QuelleBackhausz, Ágnes, und Dávid Kunszenti-Kovács. „On the dense preferential attachment graph models and their graphon induced counterpart“. Journal of Applied Probability 56, Nr. 2 (Juni 2019): 590–601. http://dx.doi.org/10.1017/jpr.2019.34.
Der volle Inhalt der QuelleBackhausz, Ágnes, und Balázs Szegedy. „Action convergence of operators and graphs“. Canadian Journal of Mathematics, 17.09.2020, 1–50. http://dx.doi.org/10.4153/s0008414x2000070x.
Der volle Inhalt der QuelleMarkering, Maarten. „The Large Deviation Principle for Inhomogeneous Erdős–Rényi Random Graphs“. Journal of Theoretical Probability, 14.06.2022. http://dx.doi.org/10.1007/s10959-022-01181-1.
Der volle Inhalt der QuelleJanssen, Jeannette, und Aaron Smith. „Reconstruction of line-embeddings of graphons“. Electronic Journal of Statistics 16, Nr. 1 (01.01.2022). http://dx.doi.org/10.1214/21-ejs1940.
Der volle Inhalt der QuelleDissertationen zum Thema "Probability-Graphons"
Weibel, Julien. „Graphons de probabilités, limites de graphes pondérés aléatoires et chaînes de Markov branchantes cachées“. Electronic Thesis or Diss., Orléans, 2024. http://www.theses.fr/2024ORLE1031.
Der volle Inhalt der QuelleGraphs are mathematical objects used to model all kinds of networks, such as electrical networks, communication networks, and social networks. Formally, a graph consists of a set of vertices and a set of edges connecting pairs of vertices. The vertices represent, for example, individuals, while the edges represent the interactions between these individuals. In the case of a weighted graph, each edge has a weight or a decoration that can model a distance, an interaction intensity, or a resistance. Modeling real-world networks often involves large graphs with a large number of vertices and edges.The first part of this thesis is dedicated to introducing and studying the properties of the limit objects of large weighted graphs : probability-graphons. These objects are a generalization of graphons introduced and studied by Lovász and his co-authors in the case of unweighted graphs. Starting from a distance that induces the weak topology on measures, we define a cut distance on probability-graphons. We exhibit a tightness criterion for probability-graphons related to relative compactness in the cut distance. Finally, we prove that this topology coincides with the topology induced by the convergence in distribution of the sampled subgraphs. In the second part of this thesis, we focus on hidden Markov models indexed by trees. We show the strong consistency and asymptotic normality of the maximum likelihood estimator for these models under standard assumptions. We prove an ergodic theorem for branching Markov chains indexed by trees with general shapes. Finally, we show that for a stationary and reversible chain, the line graph is the tree shape that induces the minimal variance for the empirical mean estimator among trees with a given number of vertices