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Nadia Belmabrouk

Publications and source records attributed to Nadia Belmabrouk.

2 recordsLinked to original sources

Invasion dynamics with vanishing fitness for a quasi-critical birth-death process

We study the invasion dynamics of populations exhibiting positive density-dependent effects. We start with a single individual and consider a single-type birth and death process. The initial individual growth rate vanishes but it increases with the population density, proportionally to the number of individuals divided by a scaling parameter $K$. Before reaching the macroscopic scale~$K$, the population process is almost critical. %{\color{red} Although the process remains asymptotically critical throughout the invasion phase, three distinct dynamical regimes emerge.} We prove that the probability for the population to reach the macroscopic level $K$ decreases as $1/\sqrt{K}$ as $K$ goes to infinity. We also describe the associated trajectories and show that invasion can be split into three time periods. First, the process needs to escape from zero, and conditioning on survival, it grows linearly until the order $\sqrt{K}$. The scaled process is approximated by a diffusion, as for critical branching process, with an additional drift term coming from cooperation, which breaks the branching property. Second, in intermediate scale $\sqrt{K}$, we observe another diffusion, surviving with positive probability, without conditioning. Finally, beyond $\sqrt{K}$ scale, the process can be approximated by a classical macroscopic ODE limit. The proof of the first phase involves change of probability and characterization of uniform integrability of martingales, while the two other phases rely on uniform approximations on polynomial time scales.

math.PR

Long-time asymptotics for multivariate Hawkes processes with long-range interactions

We consider a system of interacting particles on an infinite graph, modeled by a multivariate Hawkes process with long-range interactions, where the interaction strength decays as a power law of the inter-particle distance with exponent $1+α$. This model is more intricate and realistic for some applications, such as neural networks, where long-range connections are present. Our main focus is to characterize the long-time behavior of the system depending on the range of the interactions. These results correspond to laws of large numbers. We prove that long-range interactions affect the limiting behavior in the subcritical case but not in the supercritical case. The proofs of our results use properties of $α$-stable laws, and Tauberian methods for Laplace transforms.

math.PR