When artificial parameter evolution gets real: particle filtering for time-varying parameter estimation in deterministic dynamical systems
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Publication:5055689
DOI10.1088/1361-6420/aca55bOpenAlexW4309928345MaRDI QIDQ5055689
Publication date: 9 December 2022
Published in: Inverse Problems (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2204.00074
parameter estimationdynamical systemstime-varying parametersstate-space modelssequential Monte CarloBayesian inferenceonline estimation
Filtering in stochastic control theory (93E11) Estimation and detection in stochastic control theory (93E10)
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- On particle methods for parameter estimation in state-space models
- Vectorized and parallel particle filter SMC parameter estimation for stiff ODEs
- Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters
- Nonlinear dynamics of a spur gear pair with time-varying stiffness and backlash based on incremental harmonic balance method
- Closed form solution of a periodically forced logistic model
- Statistical and computational inverse problems.
- Linear multistep methods, particle filtering and sequential Monte Carlo
- The MATLAB ODE Suite
- A First Course in the Numerical Analysis of Differential Equations
- Filtering via Simulation: Auxiliary Particle Filters
- Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments
- An approach to periodic, time-varying parameter estimation using nonlinear filtering
- NONLINEAR DYNAMICAL SYSTEM IDENTIFICATION FROM UNCERTAIN AND INDIRECT MEASUREMENTS
- Using Monte Carlo Particle Methods to Estimate and Quantify Uncertainty in Periodic Parameters (Research)
- An Introduction to Sequential Monte Carlo
