Poster - Functional Stable Limit in Random Connection Hypergraphs
Aug 4, 2025
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0 min read
Abstract
We investigate a dynamic random connection hypergraph model based on a bipartite connection structure, in which nodes and hyperedges are modeled by two independent marked Poisson point processes. Nodes are equipped with birth-death dynamics, while hyperedges are temporally localized. Then, edges are formed under spatial and temporal constraints influenced by the vertex marks. In this system, we focus on the edge count process as a function of time within a growing spatial observation window. Under suitable assumptions, we show a functional stable limit theorem the properly rescaled and centered edge count process converges in distribution to a non-Gaussian, heavy-tailed limit in the Skorokhod space.
Date
Aug 4, 2025 4:00 PM — 5:15 PM
Location
Aarhus University - Aarhus Institute of Advanced Studies
6B Hoegh-Guldbergs Gade, Aarhus C, 8000
Authors
Quantitative Researcher
I am a PhD researcher in Mathematics with experience in stochastic modeling, probabilistic analysis, and large-scale simulation, supported by Python/C++ model development.
Previously, I worked as a machine learning researcher at Bosch, where I developed and validated predictive models with a focus on uncertainty estimation and data-driven decision-making.
I am interested in applying quantitative methods to forecasting and risk modeling in energy and financial markets.