Optimal smoothing of nonlinear dynamic systems via Monte Carlo Markov chains

DOI10.1016/j.automatica.2007.10.028zbMath1149.93343OpenAlexW2071937679MaRDI QIDQ958256

Bradley M. Bell, Gianluigi Pillonetto

Publication date: 3 December 2008

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2007.10.028

zbMATH Keywords

state estimationstochastic processesnonlinear time seriesBayesian estimationiterated Kalman smoothing filterstochastic fed-batch bioreactor

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Filtering in stochastic control theory (93E11) Estimation and detection in stochastic control theory (93E10) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10)

Related Items

A new kernel-based approach to hybrid system identification ⋮ System identification of nonlinear state-space models

Uses Software

• Gibbsit

Cites Work

• Markov chains and stochastic stability
• Measure-valued processes and interacting particle systems. Application to nonlinear filtering problems
• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Parameter estimation in stochastic grey-box models.
• Optimal scaling for various Metropolis-Hastings algorithms.
• Bayes and empirical Bayes semi-blind deconvolution using eigenfunctions of a prior covariance
• The marginal likelihood for parameters in a discrete Gauss-Markov process
• On Gibbs sampling for state space models
• Monte Carlo Smoothing for Nonlinear Time Series
• Monte Carlo sampling methods using Markov chains and their applications
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