An Overview of Sequential Monte Carlo Methods for Parameter Estimation in General State-Space Models

@inproceedings{Kantas2009AnOO,
  title={An Overview of Sequential Monte Carlo Methods for Parameter Estimation in General State-Space Models},
  author={Nikolas Kantas and Arnaud Doucet and Sumeetpal S. Singh and Jan M. Maciejowski},
  year={2009}
}
Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a… CONTINUE READING

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