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Found 4 papers, 1 papers with code
Likelihood-based generalization of Markov parameter estimation and multiple shooting objectives in system identification
We then analyze this objective function in the context of several state-of-the-art approaches for both linear and nonlinear system ID.
Bayesian System ID: Optimal management of parameter, model, and measurement uncertainty
We evaluate the robustness of a probabilistic formulation of system identification (ID) to sparse, noisy, and indirect data.
Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models
This paper proposes a probabilistic Bayesian formulation for system identification (ID) and estimation of nonseparable Hamiltonian systems using stochastic dynamic models.
Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling
The results show that using the Bayesian posterior as a training objective can yield upwards of 724 times improvement in Hamiltonian mean squared error using training data with up to 10% multiplicative noise compared to a standard training objective.
