Establishes finite-sample MSE bounds separating discretization and fluctuation errors for expected signature estimation under summable block-signature covariance, applicable to fractional Ornstein-Uhlenbeck processes across Hurst regimes.
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2026 2verdicts
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Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.
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Finite-Sample Bounds for Expected Signature Estimation under Weak Dependence
Establishes finite-sample MSE bounds separating discretization and fluctuation errors for expected signature estimation under summable block-signature covariance, applicable to fractional Ornstein-Uhlenbeck processes across Hurst regimes.
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Learning Nonlinear Dynamics: Improving the Estimation Efficiency and Reliability of Gaussian Process State-Space Models
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.