Recursive MLE algorithms for interacting particle systems employ virtual and tangent virtual particles to optimize the mean-field stationary log-likelihood from single-particle observations, with proven convergence to stationary points in the t to infinity then N,M to infinity limit.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Proves LAN for the spiking rate parameter in mean-field neuron systems with resets, yielding asymptotic efficiency and local minimax optimality of the MLE.
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Recursive Maximum Likelihood Estimation for Interacting Particle Systems using Virtual Particles
Recursive MLE algorithms for interacting particle systems employ virtual and tangent virtual particles to optimize the mean-field stationary log-likelihood from single-particle observations, with proven convergence to stationary points in the t to infinity then N,M to infinity limit.
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LAN property for the parameter of the jump rate in mean field interacting systems of neurons
Proves LAN for the spiking rate parameter in mean-field neuron systems with resets, yielding asymptotic efficiency and local minimax optimality of the MLE.