Bayesian Estimation by Sequential Monte Carlo Sampling for Nonlinear Dynamic Systems

  title={Bayesian Estimation by Sequential Monte Carlo Sampling for Nonlinear Dynamic Systems},
  author={Co - Adviser Isamu Kusaka James F. Rathman},
  • Co - Adviser Isamu Kusaka James F. Rathman
  • Published 2004
Precise estimation of state variables and model parameters is essential for efficient process operation, including model predictive control, abnormal situation management, and decision making under uncertainty. Bayesian formulation of the estimation problem suggests a general solution for all types of systems. Even though the theory of Bayesian estimation of nonlinear dynamic systems has been available for decades, practical implementation has not been feasible due to computational and… CONTINUE READING
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