Nonstandard multistep multistage methods are constructed by replacing the timestep with a bounded function, achieving the same convergence order as standard methods while preserving qualitative properties for all positive step sizes.
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2 Pith papers cite this work, alongside 151 external citations. Polarity classification is still indexing.
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Pith papers citing it
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2026 2representative citing papers
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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Some properties of high-order nonstandard multistep multistage methods
Nonstandard multistep multistage methods are constructed by replacing the timestep with a bounded function, achieving the same convergence order as standard methods while preserving qualitative properties for all positive step sizes.
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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.