StateMixNN learns particle-filter transition and proposal densities as Gaussian mixtures parameterized by neural networks, trained only on the observation likelihood, and reports improved state recovery on Lorenz 96 and Kuramoto systems.
State-space models for ecological time-series data: Practical model-fi tting
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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks
StateMixNN learns particle-filter transition and proposal densities as Gaussian mixtures parameterized by neural networks, trained only on the observation likelihood, and reports improved state recovery on Lorenz 96 and Kuramoto systems.