Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
The E nsemble K alman Filter: theoretical formulation and practical implementation
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QPCA-EnDCF is a deterministic ensemble data assimilation method that replaces stochastic observation perturbations with a spectrally regularized rank-κ update on whitened residuals, yielding better spread-skill and rank-histogram reliability than stochastic EnKF on Lorenz-96 in undersampled regimes.
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.
A variational hierarchy unifies Bayesian filtering, variational data assimilation, KL-regularized control, and Kalman methods by proving that posteriors minimize a likelihood-plus-KL objective with evidence as the global infimum.
citing papers explorer
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Error Bounds for Importance Sampling with Estimated Proposal Distributions
Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
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A Data-Consistent Approach to Ensemble Filtering
QPCA-EnDCF is a deterministic ensemble data assimilation method that replaces stochastic observation perturbations with a spectrally regularized rank-κ update on whitened residuals, yielding better spread-skill and rank-histogram reliability than stochastic EnKF on Lorenz-96 in undersampled regimes.
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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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Reinforcement Learning, Optimal Control, and Bayesian Filtering in Data Assimilation
A variational hierarchy unifies Bayesian filtering, variational data assimilation, KL-regularized control, and Kalman methods by proving that posteriors minimize a likelihood-plus-KL objective with evidence as the global infimum.