Literatura científica selecionada sobre o tema "Probability-Graphons"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Probability-Graphons".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Artigos de revistas sobre o assunto "Probability-Graphons"
Ackerman, Nate, Cameron E. Freer, Younesse Kaddar, Jacek Karwowski, Sean Moss, Daniel Roy, Sam Staton e Hongseok Yang. "Probabilistic Programming Interfaces for Random Graphs: Markov Categories, Graphons, and Nominal Sets". Proceedings of the ACM on Programming Languages 8, POPL (5 de janeiro de 2024): 1819–49. http://dx.doi.org/10.1145/3632903.
Texto completo da fonteMcMillan, Audra, e Adam Smith. "When is non-trivial estimation possible for graphons and stochastic block models?‡". Information and Inference: A Journal of the IMA 7, n.º 2 (23 de agosto de 2017): 169–81. http://dx.doi.org/10.1093/imaiai/iax010.
Texto completo da fonteZHAO, YUFEI. "On the Lower Tail Variational Problem for Random Graphs". Combinatorics, Probability and Computing 26, n.º 2 (16 de agosto de 2016): 301–20. http://dx.doi.org/10.1017/s0963548316000262.
Texto completo da fonteBraides, Andrea, Paolo Cermelli e 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.
Texto completo da fonteHATAMI, HAMED, e SERGUEI NORINE. "The Entropy of Random-Free Graphons and Properties". Combinatorics, Probability and Computing 22, n.º 4 (16 de maio de 2013): 517–26. http://dx.doi.org/10.1017/s0963548313000175.
Texto completo da fonteKeliger, Dániel, Illés Horváth e Bálint Takács. "Local-density dependent Markov processes on graphons with epidemiological applications". Stochastic Processes and their Applications 148 (junho de 2022): 324–52. http://dx.doi.org/10.1016/j.spa.2022.03.001.
Texto completo da fonteBackhausz, Ágnes, e Dávid Kunszenti-Kovács. "On the dense preferential attachment graph models and their graphon induced counterpart". Journal of Applied Probability 56, n.º 2 (junho de 2019): 590–601. http://dx.doi.org/10.1017/jpr.2019.34.
Texto completo da fonteBackhausz, Ágnes, e Balázs Szegedy. "Action convergence of operators and graphs". Canadian Journal of Mathematics, 17 de setembro de 2020, 1–50. http://dx.doi.org/10.4153/s0008414x2000070x.
Texto completo da fonteMarkering, Maarten. "The Large Deviation Principle for Inhomogeneous Erdős–Rényi Random Graphs". Journal of Theoretical Probability, 14 de junho de 2022. http://dx.doi.org/10.1007/s10959-022-01181-1.
Texto completo da fonteJanssen, Jeannette, e Aaron Smith. "Reconstruction of line-embeddings of graphons". Electronic Journal of Statistics 16, n.º 1 (1 de janeiro de 2022). http://dx.doi.org/10.1214/21-ejs1940.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteGraphs 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