Using Monte Carlo Particle Methods to Estimate and Quantify Uncertainty in Periodic Parameters (Research)

DOI10.1007/978-3-030-42687-3_14zbMath1440.93243OpenAlexW3043114689MaRDI QIDQ5118673

Andrea Arnold

Publication date: 26 August 2020

Published in: Advances in Mathematical Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-42687-3_14

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Bayesian inference (62F15) Filtering in stochastic control theory (93E11) Monte Carlo methods (65C05) Estimation and detection in stochastic control theory (93E10)

Related Items

When artificial parameter evolution gets real: particle filtering for time-varying parameter estimation in deterministic dynamical systems ⋮ Fourier series-based approximation of time-varying parameters in ordinary differential equations

Uses Software

• BioBayes

Cites Work

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