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Practical Algebraic Parameter Estimation for Noisy Data via Gaussian Process Regression
Parameter estimation for ordinary differential equation (ODE) models is a fundamental task that is often complicated by the limitations of conventional optimization-based methods. In theory, differential-algebraic approaches offer an appealing alternative: they reduce the problem to polynomial syste
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