Conditional propagation of chaos for mean field systems of interacting neurons

DOI10.1214/21-EJP580zbMath1480.60081arXiv1909.02925OpenAlexW3159805300MaRDI QIDQ2042779

Xavier Erny, Dasha Loukianova, Eva Löcherbach

Publication date: 21 July 2021

Published in: Electronic Journal of Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1909.02925

zbMATH Keywords

exchangeabilityinteracting particle systemsmartingale problempropagation of chaosempirical measurepiecewise deterministic Markov processesmean field interactionHewitt savage theorem

Mathematics Subject Classification ID

Interacting random processes; statistical mechanics type models; percolation theory (60K35) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55) Exchangeability for stochastic processes (60G09) Jump processes on general state spaces (60J76)

Related Items (8)

Well-posedness and propagation of chaos for McKean-Vlasov equations with jumps and locally Lipschitz coefficients ⋮ White-noise driven conditional McKean-Vlasov limits for systems of particles with simultaneous and random jumps ⋮ Itô-Wentzell-Lions formula for measure dependent random fields under full and conditional measure flows ⋮ Strong error bounds for the convergence to its mean field limit for systems of interacting neurons in a diffusive scaling ⋮ Impulse control of conditional McKean-Vlasov jump diffusions ⋮ Unnamed Item ⋮ A convergence criterion for systems of point processes from the convergence of their stochastic intensities ⋮ Conditional propagation of chaos in a spatial stochastic epidemic model with common noise

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