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.
Parameter estimation for the mckean- vlasov stochastic differential equation
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MVNN learns measure-dependent drift terms in McKean-Vlasov equations from particle data using an embedding network, with proofs of well-posedness, propagation of chaos, and universal approximation under low-dimensional assumptions.
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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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MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Data
MVNN learns measure-dependent drift terms in McKean-Vlasov equations from particle data using an embedding network, with proofs of well-posedness, propagation of chaos, and universal approximation under low-dimensional assumptions.