• Corpus ID: 246063838

Sequential Bayesian Inference for Uncertain Nonlinear Dynamic Systems: A Tutorial

@article{Tatsis2022SequentialBI,
  title={Sequential Bayesian Inference for Uncertain Nonlinear Dynamic Systems: A Tutorial},
  author={Konstantinos E. Tatsis and Vasilis K. Dertimanis and Eleni N. Chatzi},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.08180}
}
In this article, an overview of Bayesian methods for sequential simulation from posterior distributions of nonlinear and non-Gaussian dynamic systems is presented. The focus is mainly laid on sequential Monte Carlo methods, which are based on particle representations of probability densities and can be seamlessly generalized to any state-space representation. Within this context, a unified framework of the various Particle Filter (PF) alternatives is presented for the solution of state, state… 
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References

SHOWING 1-10 OF 95 REFERENCES
Gaussian mixture sigma-point particle filters for sequential probabilistic inference in dynamic state-space models
  • Rudolph van der Merwe, E. Wan
  • Computer Science
    2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).
  • 2003
TLDR
A novel recursive Bayesian estimation algorithm that combines an importance sampling based measurement update step with a bank of sigma-point Kalman filters for the time-update and proposal distribution generation is presented.
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
TLDR
Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
On sequential Monte Carlo sampling methods for Bayesian filtering
TLDR
An overview of methods for sequential simulation from posterior distributions for discrete time dynamic models that are typically nonlinear and non-Gaussian, and how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature are shown.
Bayesian Joint Input-State Estimation for Nonlinear Systems
This work suggests a solution for joint input-state estimation for nonlinear systems. The task is to recover the internal states of a nonlinear oscillator, the displacement and velocity of the
Particle filtering and marginalization for parameter identification in structural systems
In structural health monitoring, one wishes to use available measurements from a structure to assess structural condition, localize damage if present, and quantify remaining life. Nonlinear system
Input-state-parameter estimation of structural systems from limited output information
Following a moving target—Monte Carlo inference for dynamic Bayesian models
TLDR
This work proposes a new technique for tracking moving target distributions, known as particle filters, which does not suffer from a progressive degeneration as the target sequence evolves.
Online Noise Identification for Joint State and Parameter Estimation of Nonlinear Systems
AbstractThe quality of structural parameter identification in nonlinear systems using Bayesian estimators, such as the unscented Kalman filter (UKF), depends heavily on the assumptions about the
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