A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.
New results in linear filtering and prediction theory
4 Pith papers cite this work. Polarity classification is still indexing.
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A neural network approximates the velocity field of log-homotopy particle flow by enforcing a derived master PDE from the continuity equation, enabling unsupervised amortized Bayesian updates with reduced stiffness.
Graph State-Space Models jointly learn state-space dynamics and latent relational graphs end-to-end from time series for forecasting and structure extraction.
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A Continuous-Time Ensemble Kalman-Bucy Smoother for Causal Inference and Model Discovery
A derivative-free ensemble Kalman-Bucy smoother is developed for continuous-time data assimilation that supports Bayesian causal inference and iterative model structure identification with small ensemble sizes under partial observations.
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Physics-informed neural particle flow for the Bayesian update step
A neural network approximates the velocity field of log-homotopy particle flow by enforcing a derived master PDE from the continuity equation, enabling unsupervised amortized Bayesian updates with reduced stiffness.
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Graph State-Space Models and Latent Relational Inference
Graph State-Space Models jointly learn state-space dynamics and latent relational graphs end-to-end from time series for forecasting and structure extraction.
- Robust Filter Attention: Self-Attention as Precision-Weighted State Estimation