Sequential Monte Carlo Methods for Optimal Filtering

  • Christophe Andrieu
  • Arnaud Doucet
  • Elena Punskaya
Chapter
Part of the Statistics for Engineering and Information Science book series (ISS)

Abstract

Estimating the state of a nonlinear dynamic model sequentially in time is of paramount importance in applied science. Except in a few simple cases, there is no closed-form solution to this problem. It is therefore necessary to adopt numerical techniques in order to compute reasonable approximations. Sequential Monte Carlo (SMC) methods are powerful tools that allow us to accomplish this goal.

Keywords

Markov Chain Monte Carlo Kalman Filter Extend Kalman Filter Importance Sampling Importance Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Christophe Andrieu
  • Arnaud Doucet
  • Elena Punskaya

There are no affiliations available

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